{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53</td>\n",
       "      <td>053</td>\n",
       "      <td>072309</td>\n",
       "      <td>53053072309</td>\n",
       "      <td>723.09</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>3281057</td>\n",
       "      <td>2231211</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>POLYGON ((-122.57554 47.22952, -122.56544 47.2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>053</td>\n",
       "      <td>072311</td>\n",
       "      <td>53053072311</td>\n",
       "      <td>723.11</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>2443688</td>\n",
       "      <td>6948</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>POLYGON ((-122.53713 47.21807, -122.53713 47.2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>053</td>\n",
       "      <td>072407</td>\n",
       "      <td>53053072407</td>\n",
       "      <td>724.07</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>5422703</td>\n",
       "      <td>4068418</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>POLYGON ((-122.61063 47.30733, -122.61060 47.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>053</td>\n",
       "      <td>072408</td>\n",
       "      <td>53053072408</td>\n",
       "      <td>724.08</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>15266925</td>\n",
       "      <td>5205736</td>\n",
       "      <td>...</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>POLYGON ((-122.61886 47.29884, -122.61885 47.2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "      <td>053</td>\n",
       "      <td>072409</td>\n",
       "      <td>53053072409</td>\n",
       "      <td>724.09</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>5871772</td>\n",
       "      <td>2575269</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-122.64978 47.27622, -122.64950 47.2...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20  NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        53        053    072309  53053072309  723.09  Census Tract   G5020   \n",
       "1        53        053    072311  53053072311  723.11  Census Tract   G5020   \n",
       "2        53        053    072407  53053072407  724.07  Census Tract   G5020   \n",
       "3        53        053    072408  53053072408  724.08  Census Tract   G5020   \n",
       "4        53        053    072409  53053072409  724.09  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20   ALAND20  AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S   3281057   2231211  ...        0        0        0        0   \n",
       "1          S   2443688      6948  ...        6        0        0        6   \n",
       "2          S   5422703   4068418  ...       57        0        0       57   \n",
       "3          S  15266925   5205736  ...       60        0        0       60   \n",
       "4          S   5871772   2575269  ...        0        0        0        0   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0       25        0        0       25   \n",
       "1        0       42        0        0       42   \n",
       "2        0       30        0        0       30   \n",
       "3        0       87        0        0       87   \n",
       "4        0        0        0        0        0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-122.57554 47.22952, -122.56544 47.2...  \n",
       "1  POLYGON ((-122.53713 47.21807, -122.53713 47.2...  \n",
       "2  POLYGON ((-122.61063 47.30733, -122.61060 47.3...  \n",
       "3  POLYGON ((-122.61886 47.29884, -122.61885 47.2...  \n",
       "4  POLYGON ((-122.64978 47.27622, -122.64950 47.2...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for working with census tracts and VTD precincts\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "#tractGeomFile = gpd.read_file(\"state_map_files/fl_pl2020_t.shp\")  #Boo!  Only has geometries.  do not use\n",
    "tractPopFile = gpd.read_file(\"state_map_files/wa_pl2020_t.dbf\") #for Texas, need only this file\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "208d6842-f420-4205-859d-888e06316d7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly):\n",
    "    simplePoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.type == simplePoly.type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 1784 popn tracts for WA\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"WA\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c90b3908-e2e3-46b8-98e5-ae285eca1ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Hispanic population by total population GA voter-age population\n",
      "state pop= 10711908 VAP pct Hispanic is  0.09037630619125347\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Hispanic pop to total pop?\n",
    "print(\"this is Hispanic population by total population \"+STATE,\"voter-age population\")\n",
    "print(\"state pop=\",np.sum(tractPop), \"VAP pct Hispanic is \",np.sum(tractHisp)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract Voter Age Population\", ylabel=\"tract Hispanic pop\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractHisp, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "237df16d-af87-47ba-acb1-2ef5702c1406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is voter-age Black population by total VAP GA\n",
      "total state pop= 10711908 , VAP pct Black is  0.3027172816867175\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Black pop to total pop?\n",
    "print(\"this is voter-age Black population by total VAP \"+STATE)\n",
    "print(\"total state pop=\",np.sum(tractPop), \", VAP pct Black is \",np.sum(tractBlack)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract VAP\", ylabel=\"tract Black VAP\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractBlack, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "10b208af-8eb0-45fa-9dc0-8873eb797200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of Census tract population for WA\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for curiosity, let's plot the distribution of populations among tracts\n",
    "n_bins=50\n",
    "print(\"this is a histogram of Census tract population for \"+STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractPop, bins=n_bins)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4aac60d6-4cce-469b-b075-ad743acda38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is population by Census tract for WA\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of tract populations by area?\n",
    "print(\"this is population by Census tract for \"+STATE)  \n",
    "plt.plot(tractPop, tractArea, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f159bf55-a25e-45c0-b7ea-4c21fbce9bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n"
     ]
    }
   ],
   "source": [
    "print( type(tractGeom[0]),type(tractGeom[1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5e8f8f92-a336-43e5-8ac5-20c657cb5857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for nonPolygons in census tract data\n",
      "5 948 0.00954041820349988 nonPolygon tract no, pop, area\n",
      "1.3830859999889487e-06\n",
      "1.3608044999988245e-06\n",
      "5.39095624999952e-05\n",
      "1.850183000003754e-06\n",
      "1.0518099999993183e-05\n",
      "0.001810684065499942\n",
      "0.00010699078549999642\n",
      "9.853052999997918e-06\n",
      "0.004246756181499949\n",
      "3.1410770000042787e-06\n",
      "1.790710999999496e-06\n",
      "3.9235749999859523e-07\n",
      "0.0005019267749999812\n",
      "1.0437469999988733e-06\n",
      "5.544980999985165e-06\n",
      "0.0027832727335000483\n",
      "579 1788 0.002638890752499882 nonPolygon tract no, pop, area\n",
      "0.0026165556739998733\n",
      "1.9055880000018225e-06\n",
      "2.0429490500006518e-05\n",
      "640 2736 0.012376813375999796 nonPolygon tract no, pop, area\n",
      "1.4032389999958914e-06\n",
      "3.130329999994579e-07\n",
      "6.07059000004358e-07\n",
      "2.6122364000006113e-05\n",
      "1.6717144499987564e-05\n",
      "2.4449799999932523e-07\n",
      "3.29137950000319e-06\n",
      "4.0493049999786625e-07\n",
      "8.942357249998668e-05\n",
      "0.012238286155999815\n",
      "641 1796 0.012928625981000147 nonPolygon tract no, pop, area\n",
      "1.3831349999927905e-06\n",
      "0.0007946910889999729\n",
      "0.0001250104004999577\n",
      "1.000802949999064e-05\n",
      "0.00946765828349995\n",
      "0.002529875043500283\n",
      "643 1364 0.02001797034999983 nonPolygon tract no, pop, area\n",
      "6.414320000000249e-07\n",
      "5.391690000003932e-07\n",
      "0.02001678974899983\n",
      "644 2722 0.0186315243550002 nonPolygon tract no, pop, area\n",
      "2.788666849999024e-05\n",
      "0.01860363768650021\n",
      "769 2975 0.005439004000499942 nonPolygon tract no, pop, area\n",
      "0.004551733514000034\n",
      "3.6249499999842795e-07\n",
      "0.0008869079914999094\n",
      "774 1756 0.0039010745010000045 nonPolygon tract no, pop, area\n",
      "1.3492799999819598e-07\n",
      "3.1810170000001008e-06\n",
      "1.1402775000005222e-06\n",
      "0.003798011459000003\n",
      "8.815982050000201e-05\n",
      "1.0446999000000641e-05\n",
      "781 1472 0.0005861652529999828 nonPolygon tract no, pop, area\n",
      "0.0005286248739999915\n",
      "5.754037899999121e-05\n",
      "1056 3433 0.003027438974999988 nonPolygon tract no, pop, area\n",
      "0.0006068567004998889\n",
      "0.0024205822745000994\n",
      "1399 3891 0.00606681798650001 nonPolygon tract no, pop, area\n",
      "0.0058508284500000144\n",
      "0.00021598953649999486\n",
      "1442 2117 0.501372307800999 nonPolygon tract no, pop, area\n",
      "0.00019861754799995986\n",
      "0.501173690252999\n",
      "1691 0 0.04044016172550011 nonPolygon tract no, pop, area\n",
      "0.007447265399999966\n",
      "0.03299289632550014\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"looking for nonPolygons in census tract data\")\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[1]):  #assuming tract 1 is a single polygon\n",
    "        notPoly[m] = 1\n",
    "        print(m,tractPop[m], tractArea[m], \"nonPolygon tract no, pop, area\")\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if y < 999 :  #let's zoom in on these\n",
    "            for geom in tractGeom[m].geoms :\n",
    "                xg,yg = geom.exterior.xy\n",
    "                print(geom.area)\n",
    "                plt.plot(xg,yg)\n",
    "            plt.text(x+0.0,y+0.01,m,fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d34b5fe5-6f02-413a-8c7b-c6662fa756ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#convex-hull these right away  (decision for each state - select ones for WA)\n",
    "tractFixList = [1056,1399]\n",
    "for tFL in range(len(tractFixList)):\n",
    "    m = tractFixList[tFL]\n",
    "\n",
    "#for m in range(nTracts):\n",
    "    if notPoly[m] == 1:\n",
    "        for geom in tractGeom[m].geoms :\n",
    "            xg,yg = geom.exterior.xy\n",
    "            plt.plot(xg,yg)\n",
    "        tractGeom[m] = tractGeom[m].convex_hull\n",
    "        x,y = tractGeom[m].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "        notPoly[m] = 0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a05c0ec2-d208-45b8-8ff6-c1f1f583ad5d",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'MultiPolygon' object has no attribute 'exterior'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_391/3757461667.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtractGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcentroid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtractGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcentroid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m         \u001b[0mx2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtractGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexterior\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxy\u001b[0m  \u001b[0;31m#turn these two on to show state map\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"purple\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;34m:\u001b[0m  \u001b[0;31m# y <38.8:  #only eliminate lake tracts south of Chesapeake Bay Bridge from map\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'MultiPolygon' object has no attribute 'exterior'"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For potential later issues, identify and flag lakeshore low-population tracts.\n",
    "lakeTracts = [-99999]\n",
    "minTractPop = 5\n",
    "for m in range(nTracts):\n",
    "    if tractPop[m] < minTractPop:\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        if 0 == 0 :  # y <38.8:  #only eliminate lake tracts south of Chesapeake Bay Bridge from map\n",
    "            plt.text(x+0.1, y+0.05,m, fontsize=9)\n",
    "            # isSkippedTract[m] = 1   #not yet\n",
    "            if lakeTracts == [-99999]:\n",
    "                lakeTracts = [m]\n",
    "            else:\n",
    "                lakeTracts.append(m)\n",
    "\n",
    "print(lakeTracts)  # assigned as skipped tracts as well\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "da1f99a3-04d0-427b-95e3-d9a57836959d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAA/EklEQVR4nO3dZ3hc1bnw/f+aplHvvViy5d4LNmAM2HRjqumBQCDhCeQkPCSkJ0ACSc6b93CSQwghOSEkYAIYQgIBg8GATXHBMu69SVbvvUxdz4ctyZItjWR7ZiSN7t91zTVt7Zl7RqN7r732KkprjRBCiNBlGuoAhBBCBJYkeiGECHGS6IUQIsRJohdCiBAniV4IIUKcZagD6EtSUpLOzc0d6jCEEGLE2LJlS43WOrmv54Zlos/NzaWgoGCowxBCiBFDKVXU33PSdCOEECFOEr0QQoQ4SfRCCBHiJNELIUSIk0QvhBAhThK9EEKEOEn0QggR4oZlP3ohhPCLg2ug/igkT4SkiRCVAkoNdVRBJ4leCBG63rgfWiqP37fHQvIkSJpgJP+u27HZYArdBg5J9EKI0GWywLglcO43ofoA1OyH6v2w/x3Y+sLxctYISBpv1PqTJx4/AkjIA7N16OL3E0n0QogQpiA6w0j245b0fqq19njirzkA1fugaD3sXHm8jMkKieM6jwAmde4AJhg7BWt4cD/KGZBEL4QYnSITIfJcGHNu78cdzZ2Jv8cRQOVu2PcWaG9nIQXxYzqT/gRImQxZ840dwDA8ByCJXggRWtrqoOYg1B40kvapCouGzLnGpSe3A2oP9TgC2G+8z9GPwd1hlAmPNxL+pCth6rXGOYFhQBK9EGLk8big7qiRzLuSes0h47qt9ng5kxXSZ/rnPS1hkDrVuPTk9Ro7gJLPoXgTFH4K//4WvPM9mHINLP4RxOf6J4bTpLTWQxpAX+bNm6dlmmIhRjmtobXm5GRecwDqC0F7jpeNTDGaTRLzO6/HG9dxY8Ac5Pqs1lD6BWx7Eba/bMR5wfdgwdfBFhmwt1VKbdFaz+vzOUn0Qogh5XZA3ZGTk3ntQehoPF7OHGacGO2VzCcYj4XHDVn4PjWWwLs/gL3/hrAYmHkLzLkT0qb5/a0k0QshhpbWRn/2vpJ5w7EeJzmB6PTjyTxpQmdCz+/s624eus9wJo5thM3Pwp5/gccJY86Dq580dlJ+IoleCBF4WkNLFbRWH29u6UrstYfB0XS8rDWis3Y+vkftPN9I8GHRQ/cZAq21BnashLX/aST8S34GZ33NL4O1JNELIQLv0T56mMRmn9DUkm9cx2SG9EjUATWVwZvfgkPvG7X7a54yBmedAV+JXnrdCCH8Y8x5UPQpjL0QLn0cEsaBLWKooxqeYjLgS6/C1hWw+kfwh4VG7X7ePQHZAY7iXaoQwq/uegvGLISybRCVJkl+IErBnDvg/g2QswBWPQRvPxiQt5JEL4TwD6Vg2W/A2Qrv/Xiooxk5YjLhwh+B2QZlWwPyFtJ0I4Twn+SJsPAB+OS/YNZtRjOO6FvNQdj5Kux63ThhHZkClzwWkLeSRC+E8K/zH4Jd/4C3vg33rQerfagjGj60NubMWf87YxQtCnLPg3Puh2nLAzZlgiR6IYR/WcPhyidgxfXw6W9g8Q+HOqLhoXIPrPquccI6Ps84YT3tBohJD/hbS6IXQvhf/kVGDfXT/4bpNxjdK0ezlmr4y+VGj5plvzFGxwZx8JecjBVCBMZlvwJLOLz9baPJYjRrLgNHI5z7LZh3d9BH+EqiF0IERnQqXPyIMY3vjleGOpqhlTbDmL54/e+guSLoby+JXggROHO/AllnweofG/PEj1ZKGaNfXW3wxjeCfoQjiV4IETgmEyz7LbTXw1sPGvPIj1bJE+HCH8ChNcaEbkEkiV4IEVhp0+CinxozN750y+mt+hQqxi42rsu3B/VtB53olVJmpdRWpdRbnfdnKaU2KqW2KaUKlFLz+9muUCm1s6ucvwIXQowg5z0IVz0Jhz+C566ApvKhjmhopE4zVpta/WNjRs8gOZUa/QPA3h73fw38TGs9C3i4835/FmutZ/U3s9pQ0Vrj7TkPthAicObeCbetNJYA/PPFULV34G1CjdkCt70KrVWw4fdBe9tB9aNXSmUBVwK/AL7d+bAGYjpvxwJlfo8ugDZXbOaR9Y9Q3FyMSZmIskYxJmYMY2LGkBuTy5hY4zonOocIq0zOJIRfjL8YvrIKXrwJnr0Mbn4Bxl4w1FEFj9bGEoMA8WOC9raDmo9eKfUa8CsgGnhIa71MKTUZWA0ojCODc7XWRX1sexSox9gx/FFr/ad+3uNe4F6AnJycuUVFJ72U3zg9Ti557RKirFFcOfZK3F43jY5GipqLKGoqoqK1d/enlIgUI/n33BHEjCEzOhOryRqwOIUIWQ3F8OKNxqLa1/weZt481BEFx9vfgc1/NhYs//Kbfl0C8Yzmo1dKLQOqtNZblFIX9njqPuBBrfU/lFI3Ac8CF/fxEgu11mVKqRTgfaXUPq31xycW6twB/AmMhUcGiutMNDmbqOuoY2neUu6fdf9Jz7e72znWdIyiJiPxFzYVUtRUxPtF79PgaOguZ1ZmsqKzTtoBjIkZQ2pEKkqpQH4MIUauuGy4+1145Xb4573QeAwWPWR0QwxVXg988YKx8MrXPgrqoKkBa/RKqV8BdwBuwI7RXPM6cBUQp7XWyshojVrrmP5fCZRSjwItWuv/8lUuGCtMPbTuIVYXrmZx9mLumnoXs1JmYVIDn7Jo6GjorvkXNhZ27wSONR2jw9PRXS7cEk5OdM7xnUBsbveOIDYsMBMXCTHiuJ3w5jdhx8sw8zZjXpy4nKGOKnBW/xg2PAU3/MWYIsKP/LaUYGeNvqvpZi9wn9Z6rVLqIuDXWuu5J5SPBExa6+bO2+8DP9dav+vrfQKd6Os66thatZUHP3oQzfHPv/G2jURaI0/rNb3aS1VblZH4G48fBRQ1FVHaUopHe7rLxofF99oBdN3Oic7BbpGZ/sQoozV89Ev4+P8HNOSdD7O+BJOvAtvp/T8OW652Y86buiPGzJ5x2X576UAl+vOA/8Fo/ukA7u9s3skA/qy1XqqUGgv8s3NzC/B3rfUvBnqfQCT64uZiXt3/KmuOraG4uRig+yRsk7OJxdmLeeKCJ7Ca/d/m7vK4KGkpOakpqKixiKr2ql5l0yPTT2oKyo3JJT0qHYtJ5qATIazhGGx/2ThZWV8ItiiYeq2R9HPOCZ1mnfpCY+nAzDlwxxt+WzpwVC8OXt1WzeMbH+ej4o8wKzMLMxcyN3Uu05OmMyVxypD3qGl1tXafD+h5FFDYWEiz6/jAEovJQnZ09knnAnJjckkKT5LzASJ0aA3HNsDnf4LdnfXEa5+BWbcObVz+tOWv8O8H4No/GAu0+MGoXRy8zdXG3avvprKtkq/N+Bo3T7yZlIiUPsseazrGB8c+oNnZzIT4CcxMnklaZFqvBOrxelBKYVImOtwdtLnbiLXFYj6DkyqR1kgmJ05mcuLkXo9rral31Hcn/Z5HA+tL1+P0OrvLRlgiTjoPkBuTS05MDtG26NOOTYghob3GYKKi9cb9rPkw/tKhjcnfukYHV+8LytuFdKJ/Zf8rFDYV8uylzzI/vc+BuwC8uPdFfr3513i1F5MydQ+iSgpPwmay0eZuo93djsPjwKIsxIbF0uxsxul1olDE2+NJj0wnJzqHeHs8NrONA/UHGBMzhmvzr2VK4pRTjl0pRYI9gQR7ArNTZvd6zqu9VLRW9D4CaCpkZ/VOVheu7jUILNGeeNK5gNyYXLKjs7GZbacclxABdfRjWP0jqNhpTIZ28wrI7v9/d0RqKoe1/wn2ODjrq0F5y5BO9F2JrN3d7rPc20fexmay8drVr5ERlcGB+gNsr9rO7trdgNGDJsISQbglHIfHQaOzEbvZTlZ0Fg2OBqrbqilrKWNnzU4anY20OFvIjs6moKKAl/a9xPlZ5zMhfgL5cfnkxeYxOWHyGTW1mJSJjKgMMqIyODfj3F7POT1OSppLTtoJfFzyMTXtNb1eIz0y/eTxAbFjSItIO6OjFCFOWc0heP+nsH8VxGbD8meNXimh0CSptbFges0BKPoMNj4DXjfcuy5oPYxCuo2+zdXGXe/eRWFTId+c/U3umHJHn+VW7l/JYxsfI8oaxQ0TbuCOKXeQHJ582sm468igydnEk188yabyTZQ0l+DWbgAmJ0xm+fjlXJ53eVC7WrY4W3qdC+i5M2h1tXaXs5ls5MTk9NoBdN1PtCfK+QDhP211Rm+bz/8EFjss+jacfb+xHOFQ8HqN6QkaiqGx89JQDI0lx+93NBlllQJU/9faYzRDeT3Qo3cfmfPgil9D1tyT3v5MjOqTsVVtVXxp1ZeoaK1g1XWryI7puzvTtqptfPfj73aPip2bOpenL3rabydrHR4HxU3FbKncwsoDKzlQfwCF0TwTGxbLmJgxjIsbx7SkacxJmUO8Pd4v7zsYWmtqO2q7zwX03AEUNxfj8h6fWvbEqSJyYnLkfIA4dR4XbH4W1v0ndDTC7Dtg8Y+NxUoCSDvbqa/ZS3n1LsrrDlLZXMwFbhNZTVVGMm8qBY+z90b2WOMoIzbb6A7ZtYC31oDu/1qZjUFRygRh0RA3BjJmB2zqg1Gd6AEONxzmjnfuwO1189C8h7hhwg19Do5yepx8UvIJmys38+LeF0kOT+astLNYkL6As9PPJiMqwy/xaK3ZV7ePtSVrqWqror6jvjvJdtX68+PymZs6l7mpc5mTMofUyMD+A/TH4/VQ3lp+0g6gqKmIspayXuMQep4PyInO6W4Wyo7JJswcNiTxi2FGazjwLrz3E2P6g7wL4LJfGlMZ++G1Xa3VVFbuoLx2D+X1RyhrLqGivYqy9mrKzSYqTNBxQnfGO9q9fM+aCbFZx5N5V2KPzQK7z3Ggw8aoT/QA5S3l/HT9T9lUvomk8CTOyzyP2yffzsSEiX2W/6z0M/516F8UVBZ0t20/NO8h7px6p1/j6snpcbKndg8FlQUUVBawrWpbd5NKdnQ205OmMzZ2LOdmnMu0pGlD3oTi8DhOPh/QucOq7ajtLqdQZERldA8K63liOCMyQ84HjBYVO42RoUfXGdMAXPo4TLhs8O3wXi8tDYWUVW6nom4fZQ1HKWstp6KjjjJ3C+W4qTYp9Amvl+j2kGGJIM0aQ0Z4MulRmaTHjyM9aRJ3ffYjrh9/Pd+f//0AfODgkkTfyau9vFf4Hh8c+4B3C9/lwuwL+d2S3/ncRmvN4YbDPLL+EZqcTfz7un/7Pa7+uL1u9tfvZ0vFFgoqC9hft5+yVmOS0NyYXGYkz2BywmTmpc1jQvyEQU3hECwtzhZjqojGopOOBlpcLd3lrCYr2dHZ3U1AMj4gBDVXwkePG/O8hMfBhT80Fsg+YXCi1+2kpnoXZVW7qKg7SFnzMcpbKyh3NlDuaaNceWk+oTZu1Zo0bSLDHEGaLY6MyFTSY3JIT5hAetI00pInE+ZjtPmCFxdww4Qb+O5Z3w3EJw+qUduP/kQmZeLyvMu5PO9yzCYzbx95mzVFa7h4TF9zsRmUUtjMNnbU7ACgorWCtMi0oMRrMVmYmjiVqYlT+fLULwPQ7GzmnaPvsLZ4LRvKNvDm4TcBSAlP4bK8y7gs9zImJ0zGZrbh9ropbynH6XWSEZVBuCV4J7iibFHdsfektaauo+7kUcJNRf2OD+h5yY7OJik8icTwxKB+HnEaXO3GnOuf/gbtdtA05w4qxi+msrmUig+/R0VzKZXtNZS7mij3dlBhAvcJO/YYryYdCxnWWObaE8iIzCAtLpeMhEmkp04nMS7vjCo4Gj2sKkiBMqoSfZfdtbupazcWKv79tt/7TPQA3/rwWwDcOulWEuwJAY/Pl2hbNDdNvImbJt4EQGVrJRvLN/LBsQ94ad9LvLDnBczKTFxYHE3Opu4TqQrFgvQFLM1bypzUOcSFxRFtiw76j1wpRWJ4IonhicxJndPrOY/XQ0VbRa/zAIVNheyq2cV7Re+dtEhMhCXCeC17Yt/X4Ykk2Y2dwlCPgA51ra5WKlorqGitoLKtkoqjH1Fx4C0qLGYqkqOpsMTRXr8WPl/bvY1Za5K9kG4KY0ZYMpeFJ5ERnUVa7FgykqeQljqDqIjkgMbdNQgy1I2qRO/VXn6z5Tc8v+d5Iq2R3DnlTm6dPPCw6rTINMpay/jq9K8Ou0FGqZGpXJN/DdfkX0Ojo5GN5Rs5WH+QmvYaYsNiyY3JJcwcxqGGQ6w6uoqH1z/cva3FZCE1IpXs6Gxmp8zmrql39UqIba42NpRtwOl1Em2LZlzsONKj0gP2WcwmM5lRmWRGZfY7PqCkpYTa9lpqO2qN687bhY2FbKnc0msa6Z7CLeEk2BN87xg6r6OsUaPin3+wOtwdRhJv60zkrZXdt7vu95yuA4xFKpLC7aS5PeTb4lgYFkdaRBppMTmkJeSTmjSV5OSpmK1DO4mfFy9mFfrniEZVon/7yNv8dfdfWT5+OQ/Ne4goW1Sf5VpdrbxX+B51HXU0Ohopby2n3d3O7prdpOT0PYXCcBAbFstluUbzTV++Ofub7Knbw5GGIzQ4Gqhpr+nuUfOH7X9gxZ4VTE6cTEpECq2uVjaVb6LN3dbrNZLDk8mNzWVa4jRmpsxkZvJMksKTAv7ZbGYbY+PGMjZurM9yLq+L+o763juDjlpq2mu6bxc3F7O9ejv1HfW9eg11v5fJdtKRQV9HC4n2RGJsMSN6p+D0OI0aeM/a+AnJvK+dZ4I9obuScFbaWaRFphmJPDKN1MhUUuzJWE0Wv03YFShe7UUxcv9+gzWqEv3HJR8TbY3mkXMe8fnP+fS2p3l+z/OA8U8fb4/n1km3sjBzYbBCDQilVJ/t5mCMI3j94OscbTzK1qqthJnDuCz3Mq4adxUJ9gSanE1sqdxCUVMRhxsOs2LvCp7b/RwAmVGZzEw2kv7MlJlMiJ8wZCtvWU1WUiJS+p3TqCe3102Do6HXkcGJ1xWtFeyu3U19R32vqaZ7vt9gjxRiw2KD2lTm9rqpbqvuXfvukdQrWit69Y7qEhsWS2pEKmmRacxImmEk8a5LRBopkSkh013Wq72jotfXqEr0Y+PG8m7huzyz4xnum3lfv+W6Jhh7YM4D3DPtnhFdYxusWSmzmJUyy2eZnnPuODwO9tbuZXv1drZXb6egooBVR1cBEGYOY2LCRKYlTmNa0jTmps712xgEf7KYLCSFJw3qiMSrvcd3Cic0G/U8athft5+6jrru8RC93k9ZiLfHk2BPINoW3esSZY0i2hZNjC2GKJtxO9ra+ZwtimhrdK8ptD1eT/eOqPvS1rsmXtNec9J5jShrVHcSn5QwidTI1O6aeFpkGqkRqaPmfEZXj8PRUKMP2e6VLq+LbVXb2FC2gX11+zjSeITSltLu59+89k3yYvP63FZrzTc//CbrStZxRe4V3D39biYlTDqjeEKd1pqK1gq2V29nZ81OdtXsYm/d3u55huxmOxqN3WJnUvwkrhx7JdfmXxuSO1Gv9tLkaOrzCKG2vZb6jnqaXc00O5tpcbYY166WPpuRegq3hBNljcJsMlPTVnPSziTcEt6dxLuue9bE0yLT+m2uHK1mPj+Te6bdw7fmfGuoQzljo6p7pdvrZuX+lfxxxx+p66jDoiyMixvHjKQZXD3uahLsCaREpJAT3f9kQkopnrjwCZ784kme3/M85a3lvLD0hSB+ipFHKUV6VDrpUelcnnc5YNQ6DzceZlP5JipbKzEpk9H2X7GJh9c/TGlLKV+f+fWQW1DFpEzE2eOIs8cxLm7coLbxai+trlaanc3dlxZXS5/3XV4XKREpvWriaZFpI/58wVCwKEufR1+hJqT+ww43HOa7H3+Xg/UHmZ82n9sm3cbZGWef1vKAjY5GHB4HADdPGiUr1PuZ2WRmQvwEJsRP6PW4V3v56Wc/5Y87/sjrB19nVsosJsRPYHzceOalzRuVa+qalKm7GUf0prWmqq2K0pZS8mLz/DoPlMVkwe2VRD9ieLWXa9+4FoD/vvC/uTjnYp+1mxf3vsizO5/F4XGQYE9gUdYizk4/m0ZHI68deI0vqr4A4JaJt7Bs7LJgfIRRw6RMPL7wcZZkL2F10Wp2Vu/k/aL3AaNvfFcvjpSIFCwmCykRKURYItBoIiwRzE6ZLWvrhqAmZxNfVH7BoYZDNHQ00OHp4FjTMbZXb+/u/bUocxFPX/y0395TEv0I4/Ee7xFxyZhLfJZtc7V1LzRyXf511LTXdA82AqMXyQNzHmBOypyTFv0Q/qGU4qIxF3HRmIsA42+yv34/r+x/hUP1h9hWvY1GR2Of20ZZo8iMyiTBnkCkNRK31027p50mRxMdng6mJk7l7PSzCbeEYzVZcXldODwOOjwdONwOvNpLamQqs5JndU8W1+HuoLCpkKq2KrKissiOyT6p51DXLJ+lLaWUt5RT21FLmDmMVlcrxc3FZEdn4/a6u3daYeYwomzGyc/UiFRSI1PJjMpkbOxY4u3xo2JE5kBcXheflHzCPw/+k09KP+nu2RRuCcdmtpERmcFV466iydnEO0ff8fv7m5X5pBPWoShkEr3VbOWqsVfx7yP/5r3C97hkzCX91ugjrBE8MOcBfrPlNzQ5m3j64qdpdDRypPEIdrOdCfETRkWXq+EkwmrU1HvuWJ0eJy6vi5LmEjzag0mZqGytZF3JOqrbqqlz1FHdXo3VZMVmtpEckYzVZGVd8TreOvLWoN432hqNR3tOGi9gMVlIj0zHq724vW482kOzs7m7Oe+k17FF0+w0Bg11Td7m9DipaqtiV80u6jrqepW3mWzMTpnNdeOv49yMc/ttjqhoreBIwxGONh2lsLEQu8VunHNKnsHYWN9jCoaz+o56Xj3wKq/se4Wq9iqSwpP48tQvsyhzEZMTJvc6aezyuPjqe8ZKTP4+aaqUYjh2SPG3kEn0AN+f/30ONRziO+u+w6LMRTx10VP91prunnY3Lo+Lp7Y9xXVvXEdyeDIOjwO3182yccu4dVIILUQ8QtnMNmxmW68ZRiclTOKC7At8bufyuihrKcPhceDyurCarNjNdsLMYd1NPqUtpWwq30R5azlmZTZGEcfmkhaRRnFzMYcaDlHeWo5FWTCbzFhMFiItkWRGGyN30yLTSApPwulxdnfTrG6rxuV1kRaZdtLvzuFxUNVW1b0QfHFzMZ+WfsoPPvkBYBxFJocnE2mLxOF20OJqoaqtqtcOIsoahdPj7J4PaFriNG6fcju5MbloNF7txau9aDT1HfXdI4mr26qpd9TT6Gikw93R/XoWk4UoaxQJ4Qk8OOdB8uPzz+wPNoA2Vxufln7KB8c+4INjH+DwODg341x+cvZPWJS1qN+T8hWtFd1NqS/seYFbJt7it9lbFWrA3k6hIOS6Vzo9Tp7a9hTP7XqOa8Zdw3fmfaff2pLb6+YfB/7B+0Xv0+5pJ8wcRrOzmX11+1g+fjmPnvvoGXwKIXzzeD0UVBawr24fW6u20uxsptXVit1iJ8oaRWJ4ItnR2cxMnklebB6J9kS82tu9k/jbnr91L5TTn65+83H2OOLD4nud23B5XWyu2ExdRx1Xj7uaX5z3i17bur1uWpwt2My2k/rWa62paa/haONRWlwtzEieQaI9ETg+CMntdXOo4RBbq7byWeln3dNpxIXFcemYS7lt8m2D7pVU2FjIir0rePPwm7S720m0JzI/fT4L0hawMHPhaU80uHjlYi7IuiAk/tdH3TTFWmue2vYU/7vjf7GYLFyYfSG3TrqVs9LOGnBbj9fD3BVz8WgPd065kxsn3siYmMCsCCPEmXB5XByoP0B1ezUmZUKhjGuliLHFkB2dPWCXyzVFa3hw7YOAcVSRaE+kzd1GbXstDY6G7tpuflw+aZFp2Ew2ajtqOdJ4pLupqktXjdztdWMxWdBad7e5Z0Zlsjh7MUtyljA7ZfZpd6ltcbaw5tgaNpZvZFP5Jmraa7AoC7dOvpUH5z54yiOyl6xcwvlZ53cnerfXTbu7HYUacWMORl2i73K44TCvHXiNVUdX0eRo4plLnmFB+oIBtytuKua3X/yWD499SLglnOcuf67fBUqEGMncXjcfFX/E/rr9FDcXU99RT4Q1onvahhhbDM3OZrbXbKehowGn10mMLaZ7ofuxsWOxW+zsqN5BbUctCoXdbMfhcWA2mcmLzWN2ymwyIjP83sdfa82RxiOs2LuC1w68xnmZ5/Hk4id7jSDu+TmPNR3rbpJrdDTS5Gzilf2v9Pv64+PH8+g5jzIjeYZf4w6UUZvouzQ6Grl91e0caz7GbZNu4/5Z9w+qv/LL+17mF5t+wXX51/HzhT/3WzxCCP9auX8lj218jHum3cM90+9hb+1edtfuZm/tXg41HqKwsbDX2sdd4xb669nVJdwSzpob1xBjG/7LCY76RA/GjJTfXfddPin9hG/N/hZfm/E1n+UbHY1c+tql5Mbm8tSSp0gO8LzYQogzM/1v0096LNGeyNSkqYyLG0d+XD75cflkRWcRZY066YS5V3tpc7Vx7/v3srNmJ2D0ynr/xvdPa9BlsI2qKRD6E2mN5JZJt/BJ6SdMSZwyYPndtbtpc7fxwOwHJMkLMUJ1eDq4Nv/aAcfWgFHL/7zi8+4kD3TPSTQSEr0vo2bERmVrJU8UPAHQ59SsJ8qOykah+Ovuv3Yv0C2EGP4yozL57NbPWD5+Oa2uVj449sGgt02PTCc1IrXXSd3y1vJAhBlUo6Lpxu11c9NbN1HaXMqDcx/khgk3DOqs/78O/YtH1j9CTnQOr171qgy7F2IYW1u8lh988gNaXa3GOTht1MhzY3J5ZdkrIT/9sq+mm0HX6JVSZqXUVqXUW533ZymlNiqltimlCpRS8/vZ7nKl1H6l1CGl1A9O7yOcmQZHAwfrD3LrpFu5ZdItg+7adW3+tTx6zqPd65YKIYavC7MvZONtG3l52cucl3le9/KGhU2FfFzy8RBHN7ROpenmAWBvj/u/Bn6mtZ4FPNx5vxellBn4PXAFMAW4VSk1cAO5n3UtLLG5cvOAZVucLeyu2c2m8k3srN7ZPaDj99t+PyqGSgsx0k1NnMqvz/81z1z8DJeMuYQpiVNG/ViYQVVtlVJZwJXAL4Bvdz6sga4+R7FAWR+bzgcOaa2PdL7Oy8A1wJ4ziPmU7ak13m5u6tx+y9S01/DDT37IxvKNfT6/u3Y3Lq9r2C0OLoTo28LMhSN++U9/GWyvm98C3wN6dj7/v8BqpdR/YRwZnNvHdplAcY/7JUCfI5aUUvcC9wLk5PS/KMjp2FK5BYDl45f3W+aprU+xsXwjd0+7m5nJMwkzh7Gvbh87qndwqOEQ+XH5VLdXkxmV6dfYhBAi0AZM9EqpZUCV1nqLUurCHk/dBzyotf6HUuom4Fng4hM37+Ml+2z/0Fr/CfgTGCdjBw598LpWk9pRvaPfQ7jJCcY6sX/Z9Rdiw2JpcjT1muzoWPMxNlduZs0Na0L+pI4QIrQMpka/ELhaKbUUsAMxSqkVwFUY7fYArwJ/7mPbEiC7x/0s+m7iCahzMs5hRtIMHl7/MAWVBdw26baTpjS4edLNpEamsq9uHzXtNSTaE8mLzSM3Npc/7/wzqwtX0+5ulznEhRAjzil1r+ys0T+ktV6mlNoL3Ke1XquUugj4tdZ67gnlLcAB4CKgFNgM3Ka13u3rfQIxMraho4HffvFb3j7yNh2eDiYnTOastLOYlTKLxdmLffbE2VWzi1vfNqYt/vvSvzM9+eQReEIIMZT80r2yD18DnlBKbQd+SWf7ulIqQym1CkBr7Qb+A1iN0WNn5UBJPlDi7HE8eu6jrLlxDQ/Ne4hIayTP73meb6/9NmuK1vjctt3d3n3bZrZR0lzCTz/7KU9+8WSv+b2FEGI4OqUpELTWa4G1nbc/BU7qxqK1LgOW9ri/Clh1JkH6U2xYLHdOvZM7p97Jqwde5ecbfk52dLbPbQoqjaMLq8nKrz7/FdurtnevHH+s+Rj/dcF/BTxuIYQ4XaO6wXl28mwsysItb9/ic0DUnVPu5GvTv8aslFl4tZebJ93MXVPvAowmISGEGM5GxRQIvvRceGF60nSWj1/OORnnkBGV4XO769+8noP1B5mUMIlmZzOPLXxsUAubCCFEIMg0xQNodjbz5uE3Wbl/JUcajxDdpvm/m1OIueQSTCjwakyASYPSoL1eihuLWF/6GUqDyWs8Z0JxXsZCrrv9MaypKUGLXwghJNEPktaaA/UHKP7Zw2Su3nH6LxQRTsKNNxpTJmhAa+yTJhJ3ww1+i1UIIXqS+egHSSnFxISJ5D/2vxz8dAm6tRXr4kVYv3Q9OsyGVgoNKLMJpUwokwlMJto87ZgtViJsUbiefRHHex/R8No/QCnj4vVS39qKslqJveaaof6YQohRRmr0/fC0tFD2/R/Q8sEHmOPjibniCiLOOZuIefOwxMef0mtpt5uiO++ifYsxFcPYt/5NWH5+IMIWQoxS0nRzBlo3bqT+pZdpWbsW7XCAUoTPmkXUksVEL1mCbezYQS167Glu5sBZxkzOCXd+mfhbb8WSnIwpcmSvXCOEGB4k0fuBdjpp37WL1vUbaPnwQzr2GDNiWlJTibn8cpL+4xuYo30vOO6qquLotdfhqavrfsyanU349OnYZ0wnbvnyAV9DCCH6Iok+AFzl5bSs+5iWjz+m5aOPMCcmkvKd7xB7zdVG230/3HV1dOzeg7u2BndFJR179tC+ayfusnKiliwh++nfB/FTCCFChST6AGvftZvKxx6jfft2wmfOJPHr/4eoCy7wmfC7aK1p27CBiscex3n0KHlvvIF94oQgRC2ECCWBmutGdAqfNpUxL/2d9F/+EldFBSX33c+xO+/CXTvwIuQtH33EsbvvwXn0KNGXXkpYXm7gAxZCjCqS6P1EmUzEXX8d+WveJ+3RR2jfvp3Dl19B1W9+S8eBA/0uQxh5zjkkfvUeVEQEze+/T9kPfojj0KEgRy+ECGXSdBMgjoMHqX7ydzSvWQNaEzZhAqk/+TGR8/tcQx13fT11f3mO+hdfxNveTvTll5F0333YJ0gzjhBiYNJGP4RcVVW0fPgRtX/+M66SEmKvvZb0n/8MZet77Vl3fT11z/2V+hUr8La1EX355UbCl3Z7IYQPkuiHAW97OzXP/JHaP/4R+/TppDz0EBHzz+q3D767vp66v/2N+hdW4G1tJfrSS0n6xv3YJ07ss7wQYnSTRD+MNL37LhWPPY6n80Rt/gdrsGb2v+C4p6GB2q6E39JC9CUXk3T//dgnTw5WyEKIEUB63Qwj9mnTsGX3WOhkgC6Y5rg4Uh54gPwP1pB0//20btzE0euup3nt2sAGKoQIGZLog6h95y6OXr8cx8GDpD36KJP27Maanj6obc2xsSR/65vGEUBODjVP/6HfnjxCCNGTJPogKn/4YUyRkeS98S/ib7l5UAOqTmSOiSHxK3fRsWMHbZs3ByBKIUSokUQfRM7CQqLOW4gtK+uMXif2uuswJyRQ++yzfopMCBHKJNEHUfSSJTS8/k/qVryI9nhO+3VMdjvxt3+J1nUf07H/gB8jFEKEIkn0QZT2s58Rec45VD7+OIevWErN//4vHfv7HzXrS8Jtt6HCw6n7y18CEKkQIpRI98og01rT/N771D3/fPdCJObYWGzjxmHLzcUcF4c5JhpltaLdHrSjA3d1Ne7aOhLv/goR8473nqr45S+p//tL5L//3qBP6gohQpP0ox+mXOXltK5fT/u27TiPHsVZVISnqclY4KSLUpiTEvFU1xA2aRK5f38RU0SEsX1pKYcuvYyEO+4g9QffH6JPIYQYDmTN2GHKmp5O3PLlxC1f3utx7XSiXS6wWlFmM8pspv7VV6n46cO0rFtHzBVXGNtnZhKzdCkNK1eSdN/XMcfGDsXHEEIMc9JGPwwpmw1TZCQmmw1lNgPGiVxMJlo++bRXm37iPXfjbWuj/uVXhipcIcQwJ4l+hLAkJpLwlbtofP11iu/5Kq6yMgDskyYRed551L3wAt6eTT5CCNFJEv0IkvLQQ6T+5Ce0FRRQ8bOfdz+e+NV78NTU0PjGG0MYnRBiuJJEP4IopUi4/Usk/p97aVm3jo4DRh/6iAULsE+bRt1fnjuj/vlCiNA06JOxSikzUACUaq2XKaVeAbrmzI0DGrTWs/rYrhBoBjyAu7+zwmLw4m+7jc8+aODD/y4hgw2AwhOxBK9uY88v12FOSgKgqyk/PNrKBbdOxGyR/boQo9Gp9Lp5ANgLxABorW/uekIp9QTQ6GPbxVrrmtOKUJzEEh+PK38ONEEZ2US661DWcIgDV5sJc007oFAm8Lg1hTtqyJmSSP7clKEOXQgxBAaV6JVSWcCVwC+Ab5/wnAJuApb4PTrRr+t/dRkv/HgDcakRXPtg/1+916t54cfr2f1JqSR6IUapwR7L/xb4HuDt47lFQKXW+mA/22rgPaXUFqXUvaceouiL2WxixuIsSvfXU13c3G85k0kxdVEGJfvqqa9oDWKEQojhYsBEr5RaBlRprbf0U+RW4CUfL7FQaz0HuAL4hlLq/H7e516lVIFSqqC6unqgsAQwdVEGANs/KPZZbuLZxvQIh7+oCnhMQojhZzA1+oXA1Z0nVV8GliilVgAopSzA9UC/o3W01mWd11XAP4H5/ZT7k9Z6ntZ6XnJy8il9iNGqsbodgI5Wl89y0Ql2wiItVB9rCUZYQohhZsBEr7X+odY6S2udC9wCfKi1vr3z6YuBfVrrkr62VUpFKqWiu24DlwK7/BK5oL3ZSPBTF/W/5myXmMRwjmyrxuPpq/VNCBHKzrS/3S2c0GyjlMpQSq3qvJsKfKqU2g58DryttX73DN9TdEobG0NkrI11L+6jqabdZ1mljOvWBhk9K8Roc0qJXmu9Vmu9rMf9u7TWz5xQpkxrvbTz9hGt9czOy1St9S/8E7YACIuwctW3ZuF2eXnjf7bhaHf3W7a62Gi26ToKEEKMHjKCZgTTWnN4azUup4e2RgcdLb2TuMfjpXhPHfs2lDP5nDQAinbKcAYhRhuZpngEKz/UwOa3jjJ2djKLbppAVHxYr+c/eeUguz8u7fXY/k0VzLl8DBarOZihCiGGkNToR7CWeqO9/awr805K8gAT56eSMT6O+LQIknOiiUoIo6mmg3f+sDPYoQohhpDU6Eew1LxYlIKCVUe57KvTUCbV6/n0/Diu+86cXo8VrDrKpjePUn2smeSc6GCGK4QYIlKjH8Fik8M5+7pxHP6imk3/PjKobSadYwyyOrJNBqUJMVpIjX6Em31JDg2VbWx5p4i41Agmne17kfDIOBsoKFhViPZq4tMjSc2NIS41IkgRCyGCTRL9CKeU4oJbJ9JU08FHL+xDKcXEBWk+yy/9+nTWv36Yre8dw+s15jKetzSXBVePDVbYQoggUj3XHx0u5s2bpwsKCoY6jBGlo9XFqj/soPxQIyljohl/Vir5c1P7PEnbxeP20lDVxrb3jrFvYwXnXDeOOZeNCWLUQgh/UUpt6W+9D0n0IcTr8bLr4zL2bSin+lgzKMgcH8f0C7MYN6f/KYq1V/P+c3s4uLmSK74+nbGzZK4hIUYaX4lemm5CiKlz6uIZi7NoqGzjwOZKNr91lNrSVp+JXpkU4+elcHBzJWtf3EdjdTtFu2qJSwknJTeG/Lkp2OzyUxFipJJeNyEqLjWC+cvyWHDNWDpaXVQebfJZvnhPHWBMkbD+H4doa3RwsKCKj17Yx4qHN7J3fXkwwhZCBIBU00Lc1EUZ7PmkjDef3Mb8ZXlMPT+jz1Gx5900nknnpuPq8BCdaCc60Q4aKo40sv71w3z4/F5sdrPPIwMhxPAkbfSjQFNNOx++sI/S/fVExYcxdnYyWZMSyBwfhy184H29x+Xln//9BXXlrdzyk/nEJIUHIWohxKmQk7ECgOJ9dWx77xilBxvwuLygIDYpnAu+NJHsSQk+t22qaeflxz8nOTuaax+cfdIoXCHE0JJEL3pxuzxUHGmi/FADez4tw2RW3Pbo2Zgtvk/Z7F1fxofP7+Pc5fnMviQnSNEKIQbDV6KXk7GjkMVqJmtiPGddmceFt0+iqaaDXSfMctmXSeekkzcziQ3/PMyx3bVBiFQI4Q+S6Ee5nCkJZE6MZ/PbR2mp7/BZVinFxV+ZQkJ6JO89u3vAtWqFEMODJPpRzphCYQJet+adP+4y2u59sNktXPyVyTja3XyxuihIUQohzoQkekF8WiQX3TWZqsImPn7lwIDlk7KiyZuRxJ5Py3D6WL5QCDE8SKIXAIybncKcy8ew59MyNv7rMAOdpJ+3NBdHm5vtHxYHKUIhxOmSRC+6Lbh6LGNnJ7Pl3SKqCpt9lk0ZE0PezCS2f1CM2+kJUoRCiNMhiV50M5kU4+elAtDW5Biw/Mwl2Tja3Oz+pCzQoQkhzoAketFL9pQEohPsfLLy4IC9ajImxGGPsvLpqwdpqGwLUoRCiFMliV70EhZu4dKvTqW10cG/n9yGy9F/s4xSiou+PBmAskMNQYpQCHGqJNGLk6SNjeXye6dTdayZtX/f5/PE7JjpicQk2dnzqTTfCDFcSaIXfcqbkcT8ZXkc2FTJsd11/ZZTSjHlvAwqjzbR2jBwu74QIvgk0Yt+zblsDBabiaIBpjvInZ4EQOHOmmCEJYQ4RZLoRb/MFhNxqRGUHWzoXkS8LwkZkUQn2inaJfPfCDEcSaIXPs26OIfakhafk5gppUjJiaa+QnreCDEcSaIXPqXmxgAMONVBTHI4TbXtPmv+QoihMehEr5QyK6W2KqXe6rz/ilJqW+elUCm1rZ/tLldK7VdKHVJK/cBPcYsgMZmNBUY8bt+TnUXE2PC6tc/umEKIoXEqa8Y+AOwFYgC01jd3PaGUegJoPHEDpZQZ+D1wCVACbFZKvam13nMmQYvgsUdZAWPRcF+6Fi1xdbgJG8TyhEKI4BlUjV4plQVcCfy5j+cUcBPwUh+bzgcOaa2PaK2dwMvANacfrgg2m92CJcxM+eGT9uO9tDU7AWiply6WQgw3g226+S3wPaCv4/dFQKXW+mAfz2UCPac3LOl87CRKqXuVUgVKqYLq6upBhiUCzeP24nZ4CO+s2fcnc3wcAPUVrUGISghxKgZM9EqpZUCV1npLP0Vupe/aPEBfK0j3ebZOa/0nrfU8rfW85OTkgcISQVK81xgsNXa2779JfFokAG6n77Z8IUTwDaYxdSFwtVJqKWAHYpRSK7TWtyulLMD1wNx+ti0BsnvczwJkrPwIcnhrNdYwM1mT4n2Wq++c1Cw2OTwYYQkhTsGANXqt9Q+11lla61zgFuBDrfXtnU9fDOzTWpf0s/lmYLxSKk8pZevc/k0/xC2C5Oi2asbOSsZiNfssV7ynDqWMeeqFEMPLmfajv4UTmm2UUhlKqVUAWms38B/AaoweOyu11rvP8D1FEJktJlRfDXA9uBwetr5XREpuTHcvHSHE8HFK/eC01muBtT3u39VHmTJgaY/7q4BVpxugGFpJ2VFUF7f4LLPupf1oDZVHm3j799sZNyeF/HkpAx4FCCGCQ0bGCp/SxsZSV9ZCTUn/SwuOn5dKRKyNlNwY6ira+OBve/njN9dRtKsWLSNlhRhyaqBFoIfCvHnzdEFBwVCHIYD2Ficv/fxzTAqu/MZMknOifZbXWvP0fR9137fazVx855QBe+0IIc6MUmqL1npen89JohcDqS1t4Y3/2UZ7k5PknGgmLkgjf14KkbFhfZZvrG6ndH89zg43Bz6vpL68lRt/dBYJ6ZFBjlyI0UMSvThj7c1O9m+q4MDnlVQfa8ZkUiy5czITF6T53K610cHLj31OZGwYN3x/LhabtNsLEQi+Er200YtBCY+2MeviHG760Vnc9ugC0sfH8sFf93Bkm+9RzJGxYVx81xRqS1s6T9oOv4qFEKFOEr04ZfFpkVx5v9Fev+a5PTRU+p6Hfsy0ROYtzWXfhgr2b6oIUpRCiC6S6MVpsYaZueLrM9Bas+WdwgHLn7Usj4zxcXz88gEcbb5nwhRC+JckenHaouLDmHZ+Jvs2VVBZ2OSzrMmkOO/G8bg6POz5tDxIEQohQBK9OENnLcsjLNzCF+8WDVg2OSeazAlx7PioGK9HJj8TIlhkhQhxRmx2C9MuyKTgnSJ++o01hJlNmEwnTFva4/yrzQ12D6z/tITzLsgJdrhCjEpSoxdnbNZFOXgy7bQoTZ32oMPNeMPNeMMtxiWi8xJpoSPWwj6rm49K64c6bCFGDanRizNmj7Lyte/PZ9ojq/mPJeP59iUTfJa/5qlPqW/rCFJ0Qgip0Qu/iLBZGJMYyYGK/ufE6ZIcHUZdqzMIUQkhQBK98COTAtcgTrIW17VjNSs8MuGZEEEhiV74zfy8RDYcqaWi0XezzLTMWLaXNHL+rz+itkUWExci0CTRC7+59/yxaA0PvLwVt4+a/X8un84TN86kvLGdP6w9HMQIhRidJNELv8lLiuTxa6ex6WidzwRuNZtYPjeL5XOyeH5jEeWN7UGMUojRRxK98Kvlc7M4f0Iyf9tQSFWz7yacBy4ej9aaJz84GKTohBidJNELv/veZRNpdXj46t8K6HB5+i2XFR/BlxaMYWVBCUdrWoMYoRCjiyR64XfTMmO5aHIKO0oa+fxonc+y31icT5jFxCNv7pYpjIUIEEn0IiCaO9yMSYxgYX6Sz3LJ0WH88IpJfHygmhWbjgUpOiFGF0n0IiBSosOoaXZQ1jDwidaqZqOL5fbihgBHJcToJIleBMQ3Fufj9Hj52/rCAcv+9bNCFo1P4lfXTw98YEKMQpLoRUDkJkUyMS2ag1UtA5ZNjgkjzGLCapafoxCBIP9ZImAibBbaffS66WJWCodb5qcXIlAk0YuAibFbaWgbePKyBWMT2HSkzudoWiHE6ZNELwJmWmYMB6taBpzPZkFeIk6Pl+0ljUGKTIjRRRK9CJiLJqWiNXxysMZnuVnZcYD0uhEiUCTRi4CZnB6N1azYX+l7jvrshAgSI20cGKCcEOL0DDrRK6XMSqmtSqm3ejz2TaXUfqXUbqXUr/vZrlAptVMptU0pVeCPoMXIYDGbyEmI4Gj1wNMb5CVFcmQQ5YQQp+5UlhJ8ANgLxAAopRYD1wAztNYOpVSKj20Xa619H7+LkJSXFMWRmoG7WI5NjuTDfVVBiEiI0WdQNXqlVBZwJfDnHg/fB/yn1toBoLWW/1JxkrHJkRTWtg24mtTY5ChqWpw0truCFJkQo8dgm25+C3wP6Nn/bQKwSCm1SSm1Til1Vj/bauA9pdQWpdS9px+qGIniIqw43V4cbt/96RMibAADrk4lhDh1AyZ6pdQyoEprveWEpyxAPHA28F1gpVJK9fESC7XWc4ArgG8opc7v533uVUoVKKUKqqurT+lDiOErMdJI4AMtBh5mNX6Kg5kbRwhxagZTo18IXK2UKgReBpYopVYAJcDr2vA5Rm3/pKkKtdZlnddVwD+B+X29idb6T1rreVrrecnJyaf1YcTwkxJtB45PXNafyekxALQ63QGPSYjRZsBEr7X+odY6S2udC9wCfKi1vh34F7AEQCk1AbABvU64KqUilVLRXbeBS4Fd/vwAYnhLjg4DoKrJd5OMxWQcDLo9Mie9EP52Jv3o/wKMVUrtwqjp36m11kqpDKXUqs4yqcCnSqntwOfA21rrd88sZDGSpMYMrkbfNaGZS6ZBEMLvTqV7JVrrtcDazttO4PY+ypQBSztvHwFmnmmQYuSKi7AC0NDmuzdN19kdr6wyJYTfychYEVBds1dG2Mw+y1V2Nu2kdB4BCCH8RxK9CKg2h5HoI8N8HzyWNhiJPisuPOAxCTHaSKIXAdXiMHrRDFSj7+is+dutvssJIU6dJHoRUNWdJ2ETI8N8liupN/rPx9itAY9JiNFGEr0IqINVxoyUY5MjfZY7VNXMmMQIYiMk0Qvhb5LoRUBtPdZAbLiV9FjfJ1l3lzUxOS0mSFEJMbpIohcBs+FwLf/aVspVM9Ppe3YMQ2VTB0W1bcwZExe84IQYRSTRi4DQWvOLVXvIig/nx0un+Cz7zs5yAM7Ll6kvhAgESfQiIFZsLGJXaRP3X5hPuI8eN60ONysLSshPiWJKhjTdCBEIpzQyVojBOFDZzM/+vYdF45O45azsfsvVtTq58slPKG/s4OkvzQlihEKMLlKjF37V1OHinr9tJtxm5okbZ/psm99SVE95YwfXzc5k6fT0IEYpxOgiNXrhV//8opTiunZeuffsAaczeGdXOTaziZ9dMzVI0QkxOkmiF37V3GFMXvbd13ZgUuDRGq8XPF7defv4dVOHMWp23f5qrpqZMZRhCxHSJNELv7rz3Fyqmh3UtToxmxQmZVzMJrrvd103dbgob+hgembsUIctREiTRC/8Ktpu5efXTBvqMIQQPcjJWCGECHGS6IUQIsRJohdCiBAniV4IIUKcJHohhAhxkuiFECLESaIXQogQJ4leCCFCnNJaD3UMJ1FKVQNFQx3HEEoCaoY6iGFKvpv+yXfTv9Hw3YzRWve5qMOwTPSjnVKqQGs9b6jjGI7ku+mffDf9G+3fjTTdCCFEiJNEL4QQIU4S/fD0p6EOYBiT76Z/8t30b1R/N9JGL4QQIU5q9EIIEeIk0QshRIiTRB9ESqkblVK7lVJepdS8Ho9fopTaopTa2Xm9pI9t31RK7erndXOVUu1KqW2dl2cC+TkCIVDfTefzP1RKHVJK7VdKXRaozxAop/PdKKXeVUpt79zuGaWUuY/XHZW/m8F8N53lRvTvphettVyCdAEmAxOBtcC8Ho/PBjI6b08DSk/Y7nrg78Cufl43t7/nRsolgN/NFGA7EAbkAYcB81B/3kB/N0BM57UC/gHcIr+bU/puRvzvpudFavRBpLXeq7Xe38fjW7XWZZ13dwN2pVQYgFIqCvg28HjwIg2+AH431wAva60dWuujwCFgvn+jD6zT+W601k2dj1sAGxCSvS4C+N2M+N9NT5Loh5/lwFattaPz/mPAE0DbANvlKaW2KqXWKaUWBTTCoXM6300mUNzjfknnY6HmxO8GpdRqoApoBl7rZ7vR+LsZzHcTUr8bWRzcz5RSa4C0Pp76sdb6jQG2nQr8f8ClnfdnAfla6weVUrk+Ni0HcrTWtUqpucC/lFJTe9RchoUh+m5UH48Nu9qtP7+bLlrry5RSduBFYAnw/gmbjrrfTZdBfDcj4nczWJLo/UxrffHpbKeUygL+CXxZa3248+FzgLlKqUKMv1WKUmqt1vrCE97TATg6b29RSh0GJgAFp/UhAmQovhuMmlh2j/tZQBnDjJ+/m56v26GUehOjKeL9E54bjb+bnq/b73fDCPndDJY03QwDSqk44G3gh1rrz7oe11r/QWudobXOBc4DDvSRyFBKJXf1HFBKjQXGA0eCEHrAnel3A7wJ3KKUClNK5WF8N58HPPAg6O+7UUpFKaXSO29bgKXAvj62H3W/m8F+N4Ta72aozwaPpgtwHUZNwQFUAqs7H/8J0Aps63FJOWHbXHr0kACuBn7eeXs5xgmn7cAXwFVD/VmHy3fTef/HGL0m9gNXDPVnDfR3A6QCm4Ednb+L3wEW+d0M/rsJhd9Nz4tMgSCEECFOmm6EECLESaIXQogQJ4leCCFCnCR6IYQIcZLohRAixEmiF0KIECeJXgghQtz/A272PbVZiXnCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "oceanTracts = [1094,1690,1239,607,785]\n",
    "for t in oceanTracts:\n",
    "    x,y = tractGeom[t].exterior.xy\n",
    "    plt.plot(x,y)\n",
    "    isSkippedTract[t] = 1\n",
    "                \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b0e9e246-a558-475c-8181-075a9421034e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1784 5\n"
     ]
    }
   ],
   "source": [
    "print(len(isSkippedTract),np.sum(isSkippedTract))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d63968a2-afc1-4913-863c-4c415fb9c0b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for tracts with zero area\n"
     ]
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize zero-Area tracts.  See Ohio code for more code\n",
    "isSkippedTract = [0]*nTracts\n",
    "print(\"looking for tracts with zero area\")\n",
    "for m in range(nTracts):\n",
    "    if(tractArea[m] == 0):       \n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" )\n",
    "        x2,y2 = tractGeom[m].exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        #print(tractGeom[m])\n",
    "        plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "baabb633-83ac-4315-92bd-1d6fdd9e999b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now, manually flag tracts that should be excluded from the map, such as lakeshore empty tracts\n",
    "isSkippedTract = [0]*nTracts\n",
    "#skipList = [1108,2676]   #Lake Erie and northeasternmost Susquehanna.  All others are small.\n",
    "for s in skipList:\n",
    "    isSkippedTract[s] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>PRECCODE</th>\n",
       "      <th>COUNTYNAME</th>\n",
       "      <th>ST_CODE</th>\n",
       "      <th>PRECNAME</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PRESLAR</th>\n",
       "      <th>...</th>\n",
       "      <th>G20ATGDFER</th>\n",
       "      <th>G20ATGRLAR</th>\n",
       "      <th>G20ATGOWRI</th>\n",
       "      <th>G20LANDFRA</th>\n",
       "      <th>G20LANRPED</th>\n",
       "      <th>G20LANOWRI</th>\n",
       "      <th>G20INSDKRE</th>\n",
       "      <th>G20INSRPAT</th>\n",
       "      <th>G20INSOWRI</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53057</td>\n",
       "      <td>406</td>\n",
       "      <td>Skagit</td>\n",
       "      <td>SK00000406</td>\n",
       "      <td>SEDRO WOOLLEY  6</td>\n",
       "      <td>328</td>\n",
       "      <td>428</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>310</td>\n",
       "      <td>433</td>\n",
       "      <td>0</td>\n",
       "      <td>308</td>\n",
       "      <td>426</td>\n",
       "      <td>0</td>\n",
       "      <td>377</td>\n",
       "      <td>327</td>\n",
       "      <td>4</td>\n",
       "      <td>POLYGON ((-122.22292 48.49439, -122.22292 48.4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53057</td>\n",
       "      <td>330</td>\n",
       "      <td>Skagit</td>\n",
       "      <td>SK00000330</td>\n",
       "      <td>MOUNT VERNON 30</td>\n",
       "      <td>546</td>\n",
       "      <td>444</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>506</td>\n",
       "      <td>468</td>\n",
       "      <td>1</td>\n",
       "      <td>483</td>\n",
       "      <td>475</td>\n",
       "      <td>1</td>\n",
       "      <td>605</td>\n",
       "      <td>332</td>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((-122.28693 48.41753, -122.28685 48.4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53061</td>\n",
       "      <td>10154387</td>\n",
       "      <td>Snohomish</td>\n",
       "      <td>SN10154387</td>\n",
       "      <td>BOSTIAN</td>\n",
       "      <td>252</td>\n",
       "      <td>154</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>217</td>\n",
       "      <td>188</td>\n",
       "      <td>1</td>\n",
       "      <td>234</td>\n",
       "      <td>169</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>126</td>\n",
       "      <td>2</td>\n",
       "      <td>POLYGON ((-122.14714 47.80489, -122.14345 47.8...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53061</td>\n",
       "      <td>10154310</td>\n",
       "      <td>Snohomish</td>\n",
       "      <td>SN10154310</td>\n",
       "      <td>PARADISE</td>\n",
       "      <td>330</td>\n",
       "      <td>364</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>275</td>\n",
       "      <td>418</td>\n",
       "      <td>2</td>\n",
       "      <td>292</td>\n",
       "      <td>392</td>\n",
       "      <td>1</td>\n",
       "      <td>364</td>\n",
       "      <td>297</td>\n",
       "      <td>2</td>\n",
       "      <td>POLYGON ((-122.07690 47.81182, -122.07690 47.8...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53061</td>\n",
       "      <td>10154178</td>\n",
       "      <td>Snohomish</td>\n",
       "      <td>SN10154178</td>\n",
       "      <td>BEECHER</td>\n",
       "      <td>356</td>\n",
       "      <td>326</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>311</td>\n",
       "      <td>383</td>\n",
       "      <td>2</td>\n",
       "      <td>325</td>\n",
       "      <td>364</td>\n",
       "      <td>2</td>\n",
       "      <td>397</td>\n",
       "      <td>255</td>\n",
       "      <td>11</td>\n",
       "      <td>POLYGON ((-122.09333 47.85546, -122.09323 47.8...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  COUNTY  PRECCODE COUNTYNAME     ST_CODE          PRECNAME  G20PREDBID  \\\n",
       "0  53057       406     Skagit  SK00000406  SEDRO WOOLLEY  6         328   \n",
       "1  53057       330     Skagit  SK00000330   MOUNT VERNON 30         546   \n",
       "2  53061  10154387  Snohomish  SN10154387           BOSTIAN         252   \n",
       "3  53061  10154310  Snohomish  SN10154310          PARADISE         330   \n",
       "4  53061  10154178  Snohomish  SN10154178           BEECHER         356   \n",
       "\n",
       "   G20PRERTRU  G20PRELJOR  G20PREGHAW  G20PRESLAR  ...  G20ATGDFER  \\\n",
       "0         428          14           6           2  ...         310   \n",
       "1         444          23           3           1  ...         506   \n",
       "2         154          10           0           0  ...         217   \n",
       "3         364          22           2           1  ...         275   \n",
       "4         326          17           4           0  ...         311   \n",
       "\n",
       "   G20ATGRLAR  G20ATGOWRI  G20LANDFRA  G20LANRPED  G20LANOWRI  G20INSDKRE  \\\n",
       "0         433           0         308         426           0         377   \n",
       "1         468           1         483         475           1         605   \n",
       "2         188           1         234         169           1         273   \n",
       "3         418           2         292         392           1         364   \n",
       "4         383           2         325         364           2         397   \n",
       "\n",
       "   G20INSRPAT  G20INSOWRI                                           geometry  \n",
       "0         327           4  POLYGON ((-122.22292 48.49439, -122.22292 48.4...  \n",
       "1         332           1  POLYGON ((-122.28693 48.41753, -122.28685 48.4...  \n",
       "2         126           2  POLYGON ((-122.14714 47.80489, -122.14345 47.8...  \n",
       "3         297           2  POLYGON ((-122.07690 47.81182, -122.07690 47.8...  \n",
       "4         255          11  POLYGON ((-122.09333 47.85546, -122.09323 47.8...  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/wa_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f3b9ddb-b971-472c-a353-521377f95e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "#VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #MD's precinct file in wrong CRS\n",
    "#VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7464 0.4007449681520931 3954263 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c5c57f52-b8d2-4ef4-9d02-2429ed5e59da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ID the non-polygon PRECINCTS\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        for geom in vtdGeom[p].geoms:    \n",
    "            xs, ys = geom.exterior.xy\n",
    "            plt.plot(xs,ys)\n",
    "        #plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "    else:\n",
    "        hi = \"hi\"\n",
    "        #x,y = vtdGeom[p].exterior.xy\n",
    "        #plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ea498c6a-d054-409e-92fb-cda921e2911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prep for geography triage\n",
    "#isSkippedTract = [0]*nTracts  #done above; already skipped two lakeshore tracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3ded1b1f-5629-4d8f-826b-e037e5dcb3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAA4PUlEQVR4nO3dd3yUVfb48c+ZSYd0kkAIIfReAqGrCFgQsa8rttV1v2JdXV111W3urmV/lrWvu6y69l7WhoJKV1pCryFESgiQ0EJCeub+/phJmIQJpEzL5LxfL14zTz8T48md+5znXjHGoJRSKnBZfB2AUkopz9JEr5RSAU4TvVJKBThN9EopFeA00SulVIAL8nUArnTq1MmkpaX5OgyllGozsrKyDhhjElxt88tEn5aWRmZmpq/DUEqpNkNEdja2TbtulFIqwGmiV0qpAKeJXimlApwmeqWUCnCa6JVSKsA1OdGLiFVEVovIl47lYSKyVETWi8gXIhLVyHFTRWSriOSIyP3uClwppVTTNKdFfyew2Wn5ZeB+Y8wQ4FPg3oYHiIgVeBE4DxgIXCkiA1serlJKqeZqUh29iKQA5wOPAHc7VvcDFjnefwvMAf7Y4NDRQI4xJtdxnveAi4BNrQu7Efs2QHgMRKd45PT+bOWOQyzbfrDeOhHn94Ir9fZBGlnvev+THVP/Gk77NHrt46wWwWIRgiyC1WLBagGrxeJYFqwiWK2O7SLUGEONzWAzhhobTu+d/hmDzenVk4NzN/JjcMOJPXNmT8XroXDr/c65/dxi/3nYX+0L9mU5vt6xrfbz1dvmvJ7an4HUndcA9pHhDSLC+F7xRIYFe+zz1GrqA1PPAPcBkU7rNgAXAp8BlwPdXBzXFdjttJwHjHF1ARGZCcwESE1NbWJYDbxxIVQUw6gb4fTfQof4lp2nDXp72U7+tybf12EopZrh7rP7cseUPh6/zikTvYhMBwqMMVkicqbTphuA50TkT8DnQKWrw12sc9mYMsbMAmYBZGRktKzBVVUGEZ1g+Uuw+k0YfweMuxVCOrTodG3JI5cMoayqhjkb93PpiK48fPFgQqyWej9s5zlmnNu0ruaeacq+9c9tGlnvvNC0czZsjVfXe2+r31K3GSwWwSLHW/sWi/1bQZDT+rrtjvcWDzUKPfVNwVPzA3nsu03bOq393Mb+87C/2n+na3/uLrdxvHV+fH1tnI51TsfVqm35X/jCEsqrajz4iY5rSot+AnChiEwDwoAoEXnLGHMNcA6AiPTF3rXTUB71W/opgGebnUMug+HXwPd/hfkPw4pZMPE+GHk9WD3/FclXOoQG8dLVI3nmu2yem5fD7kOlvHTNSDp1DPV1aEqpRniqe6uhU96MNcY8YIxJMcakATOAecaYa0QkEUBELMAfgH+5OHwl0EdEeohIiOP4z90WfWMS+8OV78ANcyG+N8y+B14YBRs+BpvN45f3FYtFuPucfjx3ZTrr8oq46IUf2Lz3qK/DUkq5YPDs/QZnramjv1JEsoEt2Fvp/wUQkWQRmQ1gjKkGbsd+o3Yz8IExZmPrQm6G1DHwy9lw1QcQHAEf3QD/ORO2z/NaCL5w4bBkPrx5HNU2G5e99CNzNu7zdUhKqQaMMf7TondmjFlgjJnueP+sMaav49/9xtFJa4zJN8ZMczpmtmOfXsaYR9wbfhOIQN9z4ebFcMm/ofQwvHkJvH4h7Fnl9XC8ZWhKDJ/ffhp9Ejty05tZvDg/B50IXin/YW/Re0f7eTLWYoVhM+DXmXDuY7BvPfxnEnxwHRzc7uvoPCIpKoz3bxrHRcOTeWLOVu58b43Xbv4opU7OGLzWSd9+En2toFB7Jc6da+GM+2Dbt/b++y/vguLA6+IIC7byzBXDuW9qP75Yl8/P/72U/UfLfR2WUgpt0XteWBRM/j3cuQYyboBVb8Bz6fZqnfIiX0fnViLCrWf2Zta1GWwvKOHCF5awdvcRX4elVLtV243ql330AaljIpz/JNy2AvqdB4ufgmeHwY/PQ1VgtXzPHpjEx7eOJ9hq4ef/XkrWzkO+Dkmpdqn2dllbqLoJLPG94GevwsyFkJwOc/8Az4+E1W+DLXD6tft3juKz2yZQUW1j8bYDvg5HqXaptixCW/S+kjwcrv0UfvG5vbX/2a3w0gTYMttzjyd6WbzjISpbYHwcpdocb1fAaaJvTM+JcOM8uPx1sFXBe1fCq1Nh51JfR+YWFvH+L5tSyq6uRe+l62miPxkRGHQx3LoMpj8Nh3fAf6fCO1fAfs8MwOlpOQXF/PF/G7AZ7/2SKaXqq+uj99L/hE0dvbJ9swbbK3OGzrAPmLbkWXhpvL0uf9KDENPC0Ta9pMZmWLC1gNd+3MHibQcIsVq4bEQKM0b7d9xKBaraweQaGz7c3TTRN0dIhH3445G/hCX/gOWz7OPn+OmwyEVlVXyYuZs3lu5k16FSkqJCueecvswYnaqDnSnlQ97uNdVE3xIRcXDOwzDmZljwmN8Ni7xtfzGv/biDT1btoayqhozusdw3tR/nDupMsFV765TyF9p10xZEp8BFL8K4Xx8fFnnlf+zDIo+4zifDIj81dyvPz8shJMjChcOSuX58GoO7Rns9DqVU47xdR6+J3h1qh0XetRy+ewi++i0sfREm/wEGXgIW77Wi9xaVExZs4YffTa4ro1RK+ZfjffTeuZ5+j3cn52GRg8J9MixyemoM5VU2SiqqvXZNpVTzHG/Re4cmendzHhb54n95fVjkEamxAKzaddjj11JKtYw+GRsoLFYYfqXXh0XumxRJx9AgVu084rFrKKXcQ8e6CRReHhbZahGGdYvWFr1SfkyHQAhUXhwWOb1bLFv2FVNaqf30Svmj2qmrLRZt0QemRodFfsFtwyKP6B5Djc2wdndgjauvVKCwOVr0Vu2jD3AnDIv8e7cNi5zeTW/IKuXPahO937XoRcQqIqtF5EvH8nARWSYia0QkU0RGN3LcDhFZX7ufuwIPGPWGRU5wy7DIsR1CGNAlim82BN7UiEoFAlvdoGZ+luiBO4HNTsuPA38xxgwH/uRYbswkY8xwY0xG80NsJ3pOhBvnw+WvQU1lq4dFvnJ0N9bvKWJd3hG3hqmUar26Fr0/dd2ISApwPvCy02oDRDneRwP57g3N/coqa7jzvdXMfCOTuRv3UV1j83VI9YnAoEvgtuWtHhb54vSuhAdbeWvZTs/EqpRqseN99P7Von8GuA9wzoy/AZ4Qkd3Ak8ADjRxrgLkikiUiMxu7gIjMdHQBZRYWFjYxrOZZmF3AZ2vyWbC1kJlvZjHxiQW8OD+HAyUVHrlei9UOi3zHapjyJ3ur/qXx8OnNcGRXk04RFRbMxenJfL42n6KyKg8HrJRqjtquG4u/JHoRmQ4UGGOyGmy6BbjLGNMNuAt4pZFTTDDGjADOA24TkTNc7WSMmWWMyTDGZCQkJDT9EzRDRbX979S7M8fyr2tG0qNTB56Ys5Xxj83jN++tJmvnIf+adal2WOQ718D422HDJ/Ybtt88CMcOnvLwq8d0p7zKxier8jwfq1KqyWw2/xvrZgJwoYjsAN4DJovIW8B1wCeOfT4EXN6MNcbkO14LgE8b288bxva0jxf/wCfrOHdQEm/93xi+u3siV41J5fvNBVz20lLOf24J767Y5V816LXDIt+xCob+3D4s8nPDYeETUHms0cMGd41meLcYXlqwnd2HSr0Xr1LqpI730ftJi94Y84AxJsUYkwbMAOYZY67B3ic/0bHbZGBbw2NFpIOIRNa+B84BNrgp9mYpOFrO1S8vByA2IoRqx1/U3okdeejCQSx7cAqPXjIEmzE88Ml6xjz6PX/9YhO5hSW+CNe12mGRb1kKaafbh0V+Lh1Wvgw1rrtn/n7ZECqqbZz++Hw27NG6eqX8QW3XjdXfyitduBF4SkTWAo8CMwFEJFlEZjv2SQKWOPZZAXxljPmmNQG31B8/28Cew2W8cl0G780ce8IEHB1Cg7hqTCpf33k6H948jjP7JfLmsh1Mfmoh176y3L9u3tYOi3zDXIjrZR8W+cXR9tmubPVj7N85it+c1QeA6c8v8UW0SqkGalv0fjnxiDFmAbDA8X4JMNLFPvnANMf7XGBYa4NsrbLKGuZtKeD68WlMGZB00n1FhFFpcYxKi6OgeADvr9jNOyt2MfPNLLrGhHPVmFSuGNXNP6biqx0Wedtc+zj4H90AUwtg7C11u1RW23h3hf0G7rqHzvFRoEopZ8bfum4Cgc0Yqm2G8JDmzbOSGBnGr6f0YfF9k/jXNSNJ6xTBE3O2Mu6x77nTX27e1g6LPP1p+3LHxHqbZy3aTvb+El69PoOoMO/PeKWUOlFt54C3En27mGGqQ2gQQ7pG8/X6vdx9dt9mHx9ktTB1cGemDu5MTkEJby3bycdZeXy2Jp+BXaK4dlx3LhqeTEQz/5C4VfYcECv0mlK3KrewhOfm5XD+0C5M7n/ybzJKKe/xywemAsHZA5LYVlDC9lbeXHW+efvIJYPr3bz9yxcbW33+Fts2F1LHQngMYP9q+OCn6wkNsvDnCwb6JiallEt+O9ZNW3fF6G5EhgZx4+uZVFS3btAwsH9LuHpM93o3b99atpMpTy3kqv8sY/b6vVR56+ZtUR7s3wB9jvfBf5iVx7LcQzxw3gASI8O8E4dSqkmMlx+YahddN2Dvb39g2gAe/HQ9G/YcZWT3WLec1/nmbWHxQD7I3M07y3dx69urSIwMZcaobswYnUpyTLhbrufStrn2177nAnCgpIJHvtrMqLRYZozq5rnrKqVaxNtdN+0m0QP06xwJwLq8I25L9M4SIkO5bVJvbp7Yi4XZBby1bBfPz8/huXk59O8cydie8YztGcfoHvHEdQhx34Wz50J0KiT0Z3thCY9+tZnSymoeu3SI174aKqWarsbm3aqbdpXon/42m5iIYM4d1Nmj17FahMn9k5jcP4ndh0r5fG0+S7cf5P2Vu3ntxx0AdYl/TI84RveII76F5Zq2yjJM7gLWxk/j3n8sZHuh/UnZ+8/rT+/ESHd9JKWUG9WNdeOlhli7SfQV1TUsyTnArWf28mw3SgPd4iK4bVJvbpvUm8pqG+v3HGFZ7iGW5dZP/P2SIhnTM46xPeMZ3SPupHX6ldU2luUeZO6mfRRv+IZnq8t4Ma8XnXuE8YtxaZw9MMmrn1Ep1TxGu248o/aB0dqvTL4QEmRhZPc4RnaP47ZJvamqsbEur4hluQdZ/tMhPsrK442l9mGF+yR2JD01hr5JkfRNiqRHpw6szTvC3I37mb+lgOKKasKDrfwzbgPVJWE8ddftxERH++yzKaWaztujV7aLRL//aDm3v7MKgC37in0czXHBVgsju8cysnsst02Cqhob6/c4En/uIeZtKeCDzPojT8Z3CGHakC6cMyiJCb3iCfvnfdBzoiZ5pdqQGi+PXhnwid4Ywz0frmXDnqP85cJBXDAs2dchNSrYamFEaiwjUmO59Uz7uoMlFWTvLyH3QAl9EiMZ2T32+EBIhdlwZCdMuMNnMSulms94eeKRgE/02wpKWLztAL+fNoDrxqf5Opxmi+8YyriOoYzrFX/ixm1z7K99zvVuUEqpVvH2zdiAf2DqSKl9+N6+nQOwAiV7DiQOhBitlVeqLdEhENysb1JHgizC4mzPTE/oM+VFsGtpvadhlVJtQ03dMMXaoneLmIgQzh6YxEer8iip8KNZo1pr+3ywVdc9DauUaju83Ucf8Ike4OaJvSgqq+Khzzf6OhT32TYXwmIgxWczMyqlWqi23Nuf5oxt84Z1i+GGCT34ZFUeOw40Psdqm2Gz2RN97ylgDfj76UoFnNqneXTiETe76Yye2Ay8vnSHr0Npvb2r4VihVtso1UbZvDxhUbtJ9AmRoYRYA+TjZs8FBHqf5etIlFIt4O1hipuc+UTEKiKrReRLx/JwEVkmImtEJFNEXHYWi8hUEdkqIjkicr+7Am+uZbmHqKyx0T8Qyiy3zYGUUdDBRW29UsrvGS9PDt6cJu6dwGan5ceBvxhjhgN/cizXIyJW4EXgPGAgcKWI+GS6o017jwIwqX/iKfb0c8X7IX819NWySqXaqtqOG79K9CKSApwPvOy02gBRjvfRQL6LQ0cDOcaYXGNMJfAecFHLw225yDD7TctjFa2fXcqncr61v2r/vFJtlr/OMPUMcB/g3O/xG2COiDyJ/Q/GeBfHdQV2Oy3nAWOaHaUb5B0qRQRSYtv48L3ZcyAyGToP8XUkSqkWMo42vbemBTpli15EpgMFxpisBptuAe4yxnQD7gJecXW4i3UubzeLyExHX39mYaH7n2INslowBqprfDdMcatVV9oflOpztve+8yml2rymdN1MAC4UkR3Yu14mi8hbwHXAJ459PsTeTdNQHuA8EEsKrrt4MMbMMsZkGGMyEhISmhh+043uEQfA37/e7NMx6Vtl11KoLNanYZVSzXLKRG+MecAYk2KMSQNmAPOMMddgT9gTHbtNBra5OHwl0EdEeohIiOP4z90SeTON7RnP9ePTeH3pTl5enOuLEFpv21ywhkCPiafeVymlHFpTWH4j8JSIrAUeBWYCiEiyiMwGMMZUA7cDc7BX7HxgjPHZOAR/vmAgQRZh9+FSX4XQOtlzIO00CO3o60iUUm7grb6FZj0/b4xZACxwvF8CjHSxTz4wzWl5NjC7NUG6i4gwsnss87cUUlRaRXREsK9DarpDuXBwG4z6la8jUUq1knjtNqxdgDwq2nT3ntuPguJyrn9tRd1DC21C9lz7qw5LrJRqpnaX6DPS4vjd1P6s3nWE9XuKfB1O022bA/G9Ib6XryNRSrUx7S7RA/xsZAohQRaemLOV8qo28ABVRQnsWKIPSSkVYLzVqdAuE31MRAh/vmAgS3IO8ItXVlBUVuXrkE7up0VQU6nDHigVILz9GEy7TPQAV4/pznMz0lm9+zA3vp7p37X12+ZASCSkunr4WCmlTq7dJnqAC4Yl89ilQ1mx4xD/Wrjd1+G4Zgxs+xZ6nQlBIb6ORinlRsZLBZbtOtEDXDaiKxcMS+Yf32azwR9vzu7fAEf3aP+8UgHE2wOYtPtELyI8fPFgIsOC+MP/NvjfzdnsOfZXLatUSrVQu0/0ANHhwTx6yRDW7D7C//tmi6/DqW/bXOgyHCKTfB2JUqqN0kTvMG1IF64Zm8rrP+7wnwnESw9B3kodxEypAKXllT5w+6Q+2Aw8MnvzqXf2hpzvwNi0f16pAKPllT6050gZABndY30ciUP2HOiQAMnpvo5EKdWGaaJ38sXafMKDrVw7rruvQ4GaanuLvvfZYNH/TEqpltMM4uRoeRXR4cFEhDRrUE/PyFsJ5Uf0aVilApj20fvA8G4x7DtazvytBb4Oxf40rCUIek32dSRKKbfzbie9HzRd/cfPRqbw9rJd3Ph6JucO6gwCg5KjuPXM3t4PJnsupI6DsGjvX1spFVC0Re8kIiSI928ayxWjurFm9xG+WreXZ79zNUOihx3ZDQUb9SEppZRbaIu+gZiIEB65ZAgAL87P4Yk5WykuryIyzIuzUW1zTDKi9fNKBTQd68YPDEuJAWDeFi/32W+bCzHdoVNf715XKeUVWkfvR8b3iicpKpQFWwu9d9GqMshdaG/Ne/u3QSkVkJrcdSMiViAT2GOMmS4i7wP9HJtjgCPGmOEujtsBFAM1QLUxJqOVMXuNxSIMSo5mU/5R7110xxKoLtOnYZVqB7xVXtmcPvo7gc1AFIAx5oraDSLyFHCyMX4nGWMOtChCHxvYJYpF2YWUV9UQFmz1/AWz50BwBKSd5vlrKaV8wi+HKRaRFOB84GUX2wT4OfCue0PzDwO6RFFtM+QUlHj+YsbY6+d7TITgMM9fTynVLjS1j/4Z4D7A5mLb6cB+Y0xjdYgGmCsiWSIys7ELiMhMEckUkczCQi/2iZ/CgC6RAN7pvln/ERzZBYMv9fy1lFLtxikTvYhMBwqMMVmN7HIlJ2/NTzDGjADOA24TkTNc7WSMmWWMyTDGZCQkJJwqLK8JCbL/iMqrPTwhSWUpfPdn6DIMBv/Ms9dSSrUrTWnRTwAudNxUfQ+YLCJvAYhIEHAp8H5jBxtj8h2vBcCnwOhWxuxVtTdLrBYP96r9+Lx9ysCpf9dBzJQKcOLlirpTZhRjzAPGmBRjTBowA5hnjLnGsfksYIsxJs/VsSLSQUQia98D5wAb3BK5l3SNCadLdJhnSyyL9sAPz8DAi6H7eM9dRynVLrW26TiDBt02IpIsIrMdi0nAEhFZC6wAvjLGfNPKa3pVtc1QVePq1oQbff8XsNXA2X/17HWUUu1Ss4ZAMMYsABY4LV/vYp98YJrjfS4wrDUB+lr+kTIOlFRy9kAPzdmalwnr3ofTfwuxfjAOvlLKa3SYYj9TY/PAfxFj4Jv7oWMSnHaX+8+vlPJLfllH356lxkWQEBnKkm0eeN5rw8f2CUam/AlCI91/fqWUQhP9KVkswpT+iSzMLqSs0o0llpWl8O2fofNQGHaV+86rlFINaKJvgovTu1JSUc2sRbnuO+mPz8PRPC2nVKod02GK/ciYHnGcP7QLT3+XzQcrd7f+hHXllBdB2oTWn08p1aboMMV+SER49orhdI0Jd898st//VcsplVJeo4m+iYKsFgZ0iWL1riOUV7Wirz4vC9a9B+Nug9g0t8WnlGp7tLzSD10/Po19R8u57e1VHCypaP4JasspOyTC6Xe7P0ClVJugXTd+7LQ+nfjzBQP5fksBT87Nbv4JNnwMeSu0nFIp5VU6OXgz/XJCDxZlF7Jyx6HmHehcTjlcyymVUt6jLfoWyEiLI6egpHndN0tfcCqn9MJMVUopv+elLnpN9C1xep9OACxu6tOyR/NhydNaTqmUAkC8PAiCJvoWGJwcTXyHEL7bvL9pB3z/V7BVazmlUsonNNG3gMUiTO6fyKLsQsyp6qP2ZMHad7WcUinlM5roW2hk91iOllfz04Fjje9kDHzzgL2c8jQtp1RK1XfKhqKbaKJvoZHdYwHI2nm48Z02fAy7l8OUP0JYlJciU0r5Pa2jbxt6JXQkKiyIVbsaSfRVZY5yyiEw/GrvBqeUUk60jr6FLBahf+cothc20nXzo6Oc8tJ/azmlUsqntEXfClHhwRSXV5+44eheWPIPGHAhpJ3m/cCUUm2C1tG3AVHhQRwtqzpxg5ZTKqVOwm+nEhQRq4isFpEvHcvvi8gax78dIrKmkeOmishWEckRkfvdFLdfiA4P5mh5g0S/ZxWsfQfG3gpxPXwTmFJKOWlOH/2dwGYgCsAYc0XtBhF5CihqeICIWIEXgbOBPGCliHxujNnUmqD9RVSYvevGZjNYLOJUTpkAp//W1+EppfycXw1TLCIpwPnAyy62CfBz4F0Xh44GcowxucaYSuA94KKWh+tfav8b1fXTb/wEdi+DyVpOqZRqnHh5nOKmdt08A9wH2FxsOx3Yb4zZ5mJbV8B57r08x7oTiMhMEckUkczCwsImhuVbRaWVAESEWuuXU6Zf4+PIlFLquFMmehGZDhQYY7Ia2eVKXLfmwfU9B5dfVowxs4wxGcaYjISEhFOF5ReKy6vpGhNOsNViH52yaDec+5iWUyql/EpT+ugnABeKyDQgDIgSkbeMMdeISBBwKTCykWPzgG5OyylAfmsC9idHy6uIiQi2l1MufhoGXAA9Tvd1WEqpNsNPhkAwxjxgjEkxxqQBM4B5xpjavomzgC3GmLxGDl8J9BGRHiIS4jj+czfE7RdCg62UVdY4yimrtJxSKdUkflte2YgZNOi2EZFkEZkNYIypBm4H5mCv2PnAGLOxldf0G12iwogr2uAop7wF4nr6OiSllDpBs4ZAMMYsABY4LV/vYp98YJrT8mxgdksD9GcZ3WM4d8XrlIfGE3b6Pb4ORymlXNInY1thivmRUZZs/nrsUjYc9NbDzEqpQOFXdfTKhaoygr//CzWJg/iUSXyYufvUxyilFODlMnodvbLFlr4IRbuwXvcFo+eHsfynQ76OSCmlXNIWfUsc3QuL/wH9p0OPMxjcNYqcghIqqmt8HZlSSp1AE31LzPsb1FTCOX8DYGCXaKpt5uSzTSmlVAM6TLG/yl8Na96uV045rFs0AB9lNfY4gVJKHSderqTXRN8czqNTnnFv3eqU2AjiOoRQWqFdN0op/6M3Y5tj0/9g11K44NkTRqfsFhtOWZUmeqVU02l5pb+pKoe5f4KkwZB+7Qmbk6LC2HmwkfljlVLKibfLKzXRN9XSF6BoF0x1PTplemosOw6WcuhYpQ+CU0qpxmmib4riffXKKV1JT40BYM1urbxRSvkXTfRN8b2jnPIko1MOTYnGahFW7TzivbiUUm2a8VInvSb6U3Eup4zv1ehuESFBdIsN56cD2k+vlDq5tjZMcWAzBr55ECLi4YxTj05ZVFZFdESwFwJTSqmm0/LKk9n0Gez6EaY/A2HRp9w9NiKEotIqz8ellFLNoC36xlSVw7d/tJdTjvhFkw7p1DGUg8cqPByYUipQ6BAIvrbsRTiyC859tMmTfcd1COFAiZZXKqVOQevo/UBtOWW/86HnxCYfFtcxhCOlmuiVUv5FE70r8/4G1RV1o1M2VWxEMIdLq7xWMqWUUk3R5EQvIlYRWS0iXzqt+7WIbBWRjSLyeCPH7RCR9SKyRkQy3RG0R+WvgdVvw9ibT1pO6UqH0CBqbIaKaptnYlNKBRRvtQmbU3VzJ7AZiAIQkUnARcBQY0yFiCSe5NhJxpgDLQ/TS4yBObXllPeeev8GwoPtfflllTWEBTetX18p1f745TDFIpICnA+87LT6FuDvxpgKAGNMgfvD87JNn8HOH2Dy75tUTtlQRIgj0esolkopP9LUrptngPsA5z6JvsDpIrJcRBaKyKhGjjXAXBHJEpGZjV1ARGaKSKaIZBYWFjYxLDeqLadMHATpTSunbKi2Fa+JXinVFMZLBZanTPQiMh0oMMZkNdgUBMQCY4F7gQ9EXA6+OcEYMwI4D7hNRFyOCmaMmWWMyTDGZCQkJDTrQ7jFsn/ayymnPgrWlj1H5tx1o5RSjfH2MMVNyWgTgAtFZBoQBkSJyFtAHvCJsZeYrBARG9AJqNccN8bkO14LRORTYDSwyI2fofWK98PipxzllGe2+DThjq6bcm3RK6X8yClb9MaYB4wxKcaYNGAGMM8Ycw3wP2AygIj0BUKAejdcRaSDiETWvgfOATa48wO4RQvLKRuqbdGXaoteKeVHWlNH/yrQU0Q2AO8B1xljjIgki8hsxz5JwBIRWQusAL4yxnzTupDdbO9aWP0WjLmp2eWUDVks9u9jNVpHr5Q6iZAge+r11kRFzeqMNsYsABY43lcC17jYJx+Y5nifCwxrbZAeUzvZd0Rci8opG6p01M+HWvU5NKVU4yJD7anXW9/+23dG2vy5vZxy0u8hPKbVp6tL9MHt+8eqlDq5xdvsvdxje8R75XrtNyNVlcPcP0LiQBhxnVtOWftEbIhVH5ZSSrlWVWPjpYXb6d85ktT4CK9cs/0m+uUvwZGd9sm+W1hO2dDOg/bZpTpHh7nlfEqpwHOsoprC4goSo8IoOFrulWu2z0RfvB8WPQX9prWqnLKhTflHSYoKJSEy1G3nVEoFlpiIEC4ansyi7EJmvtnw8STPaJ8zTM1/GKrL4ZyH3XrajflHGZTc/KETlFLty7Mz0okKC+aDzN3sPlRKtzjPduG0vxb93rWw6k23lFM6O1hSwdb9xaR3i6lb98qSnxjy5zlk7TzstusopQLDzzO6YRFh6jOLyDtc6tFrta9EXzvZd3isW8opndUm8/G9j99FX7C1gOKKan7z/mrmbdl/0uOPlFay50jZSffJKSjmi7X57Dro2V8KpZTnDUmJ5sFp/TlWWcP2wmMevVb76rrZ/AXsXALnP+WWckpnhx0zS135n+V0CLFSVlVDeZW9CudYRQ03vJbJn6YP5IbTetQdU11jY21eET/kHGDWolyOVVZz/pAuZHSPZeXOw1hEiAkPpqK6hq37ilmbVwTYH7b47/WjmNC7k1s/g1LKu9JTYwmyCK/98BMT+3pujK/2k+irK2DuHxzllNe7/fST+ydx+cjDGOzDFYcHWwkNtjK2RxwZaXHc8e5q/vrlJrYVlNA3qSM7D5by5br8ujlmzxqQRO/EjryxdAdfrttL15hwQoMsHC6tJMhqoUenDtw3tR/jesbzu4/X8et3VzP7jtO1wkepNmxw12jumNKHf3ybzd6iMrpEh3vkOu0n0S9zlFNe+6nbyimdJUSG8sTljT8E/PxV6dz/8Xo+ytpNVY0hxGphyoBEpg9NZli3aFJi7TdjbpnYi6PlVSe9OfPPq0dywfNL+PvXm3lmRrrbP4tSynvSU2MA2HWwVBN9q5QUwKInoe950GuyT0IItlp46ufDeOJnQzlcWklosJWOoSf++KMjgomOCD7puXondqR3YkeKyqo8Fa5Syktqn6j35AhZ7SPRz/sbVJe5vZyyJSwWIb5j6+vsj1VWkxp68pKsPUfKeHF+DiXl1XSNDeeq0akeL+NSSjWPzZHhF28rZGxPzwyJEPhVN3vX2cspR98EnXr7Ohq3CbFaWLPrCHuLTqzUOVZRzVNztzL5yQV8nJXH2rwjzFqUy7RnF/PZmj0+iFYp1ZizBiRySXpXXpy/nflbPDMja2An+trRKcNjYaJ7yyl97bFLh1BUVsUV/17Gp6vzWLr9IKt3HeZ/q/cw5amFPD8vh6mDOzPvnjNZeO8kFtxzJv06R3Lne2u4+/01lFZW+/ojKKUAEeG35/QF4NPVnmmIBXbXzZYvncopY30djVulp8by+g2j+fU7q7jr/bX1tvXvHMmLV6czsntc3bpucRG8N3Msz8/L4bl521i58xBP/3w4GWlxDU+tlPKyH3Lso1leOTrVI+cP3ERfW06ZMMAj5ZT+YGT3WL7/7Zls3neU8qoayqtqsFosjO8VT7CLMfGDrBbuOrsvY3rG8eAn67n2lRW8cn0G43tpPb5SvvRBZh6do8IY08MzDa/A7bpZ/i84vKNVk323BeEhVkakxjK+Vycm909iYt8El0ne2fhenfjg5nGkxIbzi1dW8OjszZRUaFeOUr6wMLuQrJ2HuWRE17pZ6twtMBN9SQEsfAL6TvVZOaW/S4wM44ObxnFJeldmLcpl8pMLWLP7iK/DUqpdKaus4YbXVgJw+cgUj10nMBP9vIf9ppzSn8V2COGJy4fx6a3jsYjw4Cfrsdl0vlulvCU0yEJ3x+Qjs9fv9dh1Ai/R71sPq96A0TOhUx9fR9MmpKfGcseUPmzae5St+4t9HY5S7YbFIsy+43RO79OJfy3MpcZDDa0mJ3oRsYrIahH50mndr0Vkq4hsFJHHGzluqmOfHBG53x1BN8oYyF3gKKe8z6OXCjQju9urknRIZaW8KyzYyiXpXSmpqGbRtkKPXKM5Lfo7gc21CyIyCbgIGGqMGQQ82fAAEbECLwLnAQOBK0VkYKsiPplqx8NDkx4MuHJKT+ud2JEhXaN5Ys5Wikp1aAWlvOkMx8iVPzgmDXe3JpWjiEgKcD7wCHC3Y/UtwN+NMRUAxhhXj3SNBnKMMbmO87yH/Y/DplbGfaLqiuPvR/7S7acPdFaL8NilQ5j+/BI+WpXHr5yGU1aqOV5d8hP/XLCdapsNm81gDNQYg80YbAaMqd89IUjtG+cX+3tpsE+9dc77Sf11ThulwT6nOodF7O8FsIgcXxb7svOrYB/HKizYCkBJRXXdcbX7WSy1y/ZzOZ/TIhBksRASZG9zL8gu5A+N/WBboal1h88A9wGRTuv6AqeLyCNAOXCPMWZlg+O6ArudlvOAMa4uICIzgZkAqakteGjAVmN/zfhVQJdTetLgrtGkp8bwxtIdXDU6lfAQq69DUm3M4m2FPDJ7M+ndYhiYHOVIavaEZrVIXcKsTbC1Kb829xvnob1M/X3s+5l6+5/qHMZFl3fdOVzsY7D/Yar9g2R/7/gDxfFl59fKGhvlVfb8kxxjHzbcZqPeH7ba97Z65zTYbFBWU1M3QOFoD9XRnzIjish0oMAYkyUiZzY4NhYYC4wCPhCRnqb+n2tXRaEu7zYYY2YBswAyMjKaf0ciJAIeKmr2Yaq+e87pxzWvLOfhrzbxyCVDfB2OaiNyC0t47OstfLtpP32TOvLK9aOIDj/5KKzKe5rS9J0AXCgi04AwIEpE3sLeOv/EkdhXiIgN6AQ4303IA7o5LacA+W6JXHnEhN6duDQ9hc/X5vPwxYPrfd1V7VPtfMg/HThGalwEI1Jj6eAYYruotIoPMnfzj2+zCbIKd0zpw8wzerocglv5zin/axhjHgAeAHC06O8xxlwjIjcDk4EFItIXCAEa3klYCfQRkR7AHmAGcJXbolceMaRrFB+vyqOwuILEKJ3BKhAVFlfwUVYexeVVXD8hjcTIsLr1+UfKKCiu4KcDJcxev++EB+msFiEtPoJjFTXsLy7HGDi9Tyce/9lQj02coVqnNX92XwVeFZENQCVwnTHGiEgy8LIxZpoxplpEbgfmAFbgVWPMxtaHrTzpsKPqJkq/egek3MISrpi1jMLiCiwCr/24g/G9OlFWVc0POQfr7TuwSxT3nNOXYd1iSIvvwE8HjrH8p4NsLzhGh9AgusWFc9aAJAZ3jfbRp1FN0axEb4xZACxwvK8ErnGxTz4wzWl5NjC7NUEq70p1TE6ydV8xw7rF+DYY5VblVTXMfDMLm83w1R2nERESxIvzc1ifV0R5dQ13ndWXgclRJEWF0jk6rK6lX6tbXERdKaBqO7QjTZ3grAFJhAdbeWvZTk30AeapuVvJKSjhjRtGMyjZ3gp/8iRzHavAoIlenSA6IpipgzvzvYdmu1GeV1ltY+fBYwRZLew+VMq2ghK27S/m/czdXDUmVVvl7YwmeuVSjc0QGhR4QyEFKpvNsPNQKQu3FrB42wGW5h6ktLKm3j4xEcFM6Z/Eg9MG+ChK5Sua6JVLS3MPMs5DExUr9zhaXsVLC7bz3ab97DpUSkW1DYDu8RFcNiKFEd1jqLFB15hw+iR1JL5DiJbLtlOa6JVLxyqqCQvWFr0/qq6x8d7K3Tz9bTYHj1VyRt8EzuyXQI9OHRnfK560Th18HaLyM5rolUsXDE3m41V53DSxF70SOvo6nHbPZjNs3neUpdsP8u6KXWwvPMaotFj++8tRDE2J8XV4ys9polcu3Tu1H5+szuO9Fbv4/fmeG3BUndqnq/P4f19vZd/RcgAGdInipatHMHVwZ+2KUU2iiV651KljKMNSYli7W8cPaqndh0pZuv0gFTU2MrrH0r9zZLMT80dZedzz4VqGdYvh3nP7Ma5XPMkx+vSpah5N9KpRCZGhZLfBGacOllSw+3AZ/TtH1g0f621fr9/LPR+u5ZhT5cvZA5N46eoRBJ1i8naA4vIq1u8p4o//28CYHnG8/X9jmnScUq5ooleNyj9SRkJkqK/DqGO/QWzFajneKjbGUF5lo6C4nG837Wfupv1k7jiEzUBIkIV7z+nH/53eo64lbYyh2mYI9lDSPFBSwaxFucxalMuwbjE8ftlQOoRa+ThrD09/l839n6zn8cuGYnH6DDabYXthCQuzC5m3pYDNe4/WDUMR3yGE565M1ySvWkUTvXKpxmbYvK+Y68Z193UozNm4j7eX72LJtkIiQoIY3i2G9NQYbMbw9fp95B44Vrdv/86R3D65D/07R/LJqj08Mnszn63dw9RBndl1qJQv1+2ltLKGfkmR3De1H1MGJLklxi37jvLqkp/435p8KqttXD4yhYcvGUxokP0bxZ1n9cFgeOa7bRw+Vkm/zpEUFFewr6ic9XuK6sYj75cUydTBXegeH0G32AhGpcXqwHKq1TTRK5cKiyuorLaRGu/bUr1/Lsjh8W+20jUmnBvP6Mmximqydh7hxfk52AyM6RHHZSNTiAoP5ow+nejuFO95gzvz1vJdfJS5myfnZhMaZOGi4ckkRYUxd+N+fvV6JqPSYpk2pAvThyY3+9uLzWZYkF3AK0t+4oecg4QFW7h8ZAq/nNCD3oknVirdOaUPwVYL/164nQXZhSR0DCUpKpRpQzqT3i2W8b3jSYmNaPXPTKmGpOG0Xv4gIyPDZGZm+jqMdq20spqhD83lxjN68rup/X0Sw/Lcg1wxaxnnDEzinw36tmtn9GlqH3xRWRVWi9SNk15eVcPLi3P5Yu1etu4vplPHEL6/+0yiI04csbO8qgaLCIUlFew/Ws6I1Fi27ivm1rez2F54jM5RYfxifHeuGp1KTETIKWOpsRn7dHMWrZhR7iMiWcaYDFfbtEWvXIoICWJ0jzhe+2EHHUODuG58mtcmkzhSWslLC7bz8ao9hAVb+MtFg07oo27uTdaGsx2FBVu5fXIfbp/chxU/HeLn/17Keyt3cdPEXgB8siqPx77eQlFpFZU1NkKDLFTW2DAGzuyXwIY9R7EIPDtjONOGdGlWn79VE7zyMk30qlHPXDGcBz5ZzxNztvJxVh7PX5VeN+KhpxQWV3DX+2tYknOAcT3juefcvh6fzGJ0jzh6J3Zk5Y5D3DSxF/O3FtSVNP5sZAoRwVaOlFURHR5MWVUNsxbl0qljCG/+agx9kyJPfQGlfEy7btQp/bj9AL9+Z3Xd4/Z9EjvSv3MkE/smuOVGoTGGL9ft5b8//MSGPUcBeOjCQVw1pgWTxLfQjFlL2bjnKFeNSeXfi3KJjQhmye8m102Z56y8qoYQq0W7XpRfOVnXjSZ61SRFpVW8vnQHH2XZpxgsc/SRj0qL5dIRKUwb0qVZk0HX2AxFZVUcq6jmoc838v2WAnondmRK/0Quz+jm8mamJ325Lp+7319LZY2NbnHhPDcjnfTUWK/GoFRraKJXbmWMYfPeYr7fvJ//rdnD9sJjhARZuH1Sby5J78r3m/eTXVDCxL4JnDMwqd7ToOVVNTz81Sa+XLeXI45a8dAgC/dN7c/149N82n9dXlVDkEW0Zl21SZrolccYY1iXV8Ssxbl8tW5v3foQq/3m5cMXD+aasd2x2QxzN+3jme+2sWVfMRcPT2Zw12hsxnD2wM700BEXlWoVtyR6EbECmcAeY8x0EXkIuBEodOzyoGN+2IbH7QCKgRqgurFAnGmib5uW5R5k2/5i0lNjGdAlil+8upwfcg6SEBmKMfanRtPiI/j9+QM5e6B7HlRSStm5q7zyTmAzEOW07mljzJNNOHaSMeZAM66l2qCxPeMZ6zRZyYtXjeD9lbvJLTxGVY2Nif0SmD40WcsLlfKyJiV6EUkBzgceAe72aEQqYMREhNTVpSulfKepd52eAe4DbA3W3y4i60TkVRFprETBAHNFJEtEZjZ2ARGZKSKZIpJZWFjY2G5KKaWa6ZSJXkSmAwXGmKwGm14CegHDgb3AU42cYoIxZgRwHnCbiJzhaidjzCxjTIYxJiMhQWeoV0opd2lKi34CcKHjpup7wGQRecsYs98YU2OMsQH/AUa7OtgYk+94LQA+bWw/pZRSnnHKRG+MecAYk2KMSQNmAPOMMdeISBen3S4BNjQ8VkQ6iEhk7XvgHFf7KaWU8pzWjHXzuIgMx94HvwO4CUBEkoGXjTHTgCTgU8cDM0HAO8aYb1oTsFJKqeZpVqI3xiwAFjjeX9vIPvnANMf7XGBYqyJUSinVKvqst1JKBThN9EopFeD8cqwbESkEdrbw8E6Avz2F648xgcbVHP4YE2hczeGPMYH74upujHFZm+6Xib41RCSzKePpeJM/xgQaV3P4Y0ygcTWHP8YE3olLu26UUirAaaJXSqkAF4iJfpavA3DBH2MCjas5/DEm0Liawx9jAi/EFXB99EoppeoLxBa9UkopJ5rolVIqwLXZRC8il4vIRhGxiUiG0/qzHWPfr3e8TnbatkBEtorIGse/RH+Iy2mfz0XE7YO+tfBn9Y2IrHUc9y/HVJI+jUtEIkTkKxHZ4jju7+6OqSVxObY9IiK7RaTEj2Ia6VifIyLPifMs7Z6PK15E5otIiYi80OCYKxzzWGwUkcfdHVMr4rrS8fNa5/j97+TLmEQk0ilXrRGRAyLyTIsuboxpk/+AAUA/7GPvZDitTweSHe8HY5/jtnZbvX39JS7HukuBd4AN/hATEOV4FeBjYIav4wIisE9LCRACLAbO83VcjuWxQBegxF9+r4AVwDjHf8Ovvfyz6gCcBtwMvOC0Ph7YBSQ4ll8HpvhBXEFAAdDJsfw48JAvY3JxfBZwRkuu3ZrRK33KGLMZoGEjxRiz2mlxIxAmIqHGmAp/jUtEOmKfonEm8IE/xGSMOepYH4Q9qbr9rn0L4ioF5jv2qRSRVUCKH8RVYYxZ5uoYX8UExGH/Y73UcdwbwMXYE7434joGLBGR3g0O6QlkG2Nqp5H7DrgM+N7HcYnjXwcROYh9buwcH8d0PDiRPkAi9sZNs7XZrpsmugxY3SDJ/9fxNeiPnvgq28K4/oZ9hq5SH8UDLn5WIjIHeyunGPjIX+ICEJEY4ALcnCCawWVcPuYcU1cgz2lbnmOdr+UA/UUkTUSCsP/x6ebbkMAYUwXcAqwH8oGBwCs+Daq+K4H3jaNp31x+3aIXke+Azi42/d4Y89kpjh0E/D/sk53UutoYs0fsk6F8DFwLvOHLuMQ+pn9vY8xdIpLW3Fg8EVMtY8y5IhIGvA1MBr71h7gcCeJd4DljHwq72TwRV2u5OSZXjZgWJYnWxHVCAMYcFpFbgPexz0H9I/ZWvk/jEpFg7Ik+HcgFngceAB72VUwNzMCer1rErxO9MeaslhwnIinYpy38hTFmu9P59jhei0XkHezTGjY70bs5rnHASLFP1RgEJIrIAmPMmT6Myfm85SLyOXARLUj0HoprFrDNGPNMS87twbhaxc0x5VG/WysFe0vVa3Gd5HxfAF8AiMhMoMYP4hruOOd2ABH5ALjfxzHhiGUYEGROnLe7yQKu68bxlf4r4AFjzA9O64Nq76I7/npPx4vTGjYWlzHmJWNMsrFP1Xga9v7LM30Zk4h0FMdUkY7W8zRgizdiOllcjm0PA9HAb7wVT1Pi8pWT/F7tBYpFZKyji/IXQGtalG4jjmo3EYkFbgVe9m1EAOwBBopI7eiPZwObfRiPsyuxf4NtudbeSfbVP+zz1OYBFcB+YI5j/R+AY8Aap3+J2O9sZwHrsN+0ehaw+jquBsem4Zmqm+b+rJKAlU4/q+extyh8HVcK9u6HzU7r/8/XcTm2Pe44xuZ4fcgPYsrA3pjZDryA40l4b8Tl2LYDOASUOPYZ6Fj/LrDJ8c/t1VytiOtmx+/WOuzfOOJ9HZNjWy7QvzXX1iEQlFIqwAVc141SSqn6NNErpVSA00SvlFIBThO9UkoFOE30SikV4DTRK6VUgNNEr5RSAe7/A94Tv0qA+RoyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic WA map\n",
    "#tractMAP = tractGeom[0] #uncomment for first time thru loop  **************\n",
    "for t in range(1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        uncomment = \"if-redone\"\n",
    "        #tractMAP = tractMAP.union(tractGeom[t])  #uncomment to build map  ********\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-125,46.)\n",
    "pt2 = Point(-123.7,47.5)\n",
    "pt3 = Point(-123.3,48.4)\n",
    "pt4 = Point(-125,49)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #clip eastern outcropping NE of Philly\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()\n",
    "#wholeMAP = tractMAP  #in case we later slice parts out\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f33de347-5473-4b11-b3ae-86691b33098d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  13 tracts w pop 29801.0  to reassign to tracts...\n",
      "[60, 62, 63, 496, 497, 498, 499, 504, 644, 645, 684, 1094, 1224, 1460, 1461, 1462, 1690, 1753]\n",
      "I have flagged  36 precincts to reassign to precincts...\n",
      "[1156, 1158, 1169, 1171, 1180, 1210, 1249, 1262, 1263, 1264, 1267, 1268, 1269, 1271, 1369, 1389, 1390, 1850, 1853, 1854, 1855, 1858, 1871, 1960, 2085, 2089, 2465, 2472, 2477, 2578, 5319, 5320, 5337, 5339, 6226, 7388, 7389, 7449, 7451]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1  #uncomment once confirmed\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            if tractGeom[t].type == tractGeom[0].type:\n",
    "                x,y = tractGeom[t].exterior.xy\n",
    "                plt.plot(x,y)\n",
    "            else:\n",
    "                for geom in tractGeom[t].geoms:\n",
    "                    x,y = geom.exterior.xy\n",
    "                    plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.2 :  # and tractGeom[t].centroid.x > -76.4 :\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1  #uncomment once confirmed\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.2 : # and vtdGeom[p].centroid.x > -76.4 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "33897c0d-3254-4a76-9b82-314a8f320eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e2595c62-579a-4af1-9552-0859524782be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9e1d7fcd-cdeb-4317-9f89-5291a9595587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d2622568-1fc9-4089-9ae4-2550ea6fbaaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  29801.0 people (VAP of 23717.0 ) and  20684.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "fe47f683-719d-4a02-9648-905bddbf28a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now work on the NNW islands\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-123.5,48.2)\n",
    "pt2 = Point(-122.8,48.2)\n",
    "pt3 = Point(-122.2, 49.01)\n",
    "pt4 = Point(-123.5,49.01)\n",
    "cutLine = LineString([pt2,pt3])\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "plt.show()\n",
    "\n",
    "cutLine = LineString([pt2,pt3])\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "cac3531a-35c7-438d-b5b9-5fcdf0745329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  68 tracts w pop 252909.0  to reassign to tracts...\n",
      "[294, 577, 580, 584, 756, 774, 776, 1480, 1733, 1734, 1762, 1769, 1775]\n",
      "I have flagged  195 precincts to reassign to precincts...\n",
      "[269, 281, 287, 293, 305, 1036, 1041, 1053, 1096, 1101, 1132, 1140, 1201, 1209, 2614, 2619, 2624, 2625, 5731, 5777, 5790, 5795, 5799, 5800, 7341, 7347, 7350, 7359]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            #isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            if tractGeom[t].type == tractGeom[0].type:\n",
    "                x,y = tractGeom[t].exterior.xy\n",
    "                plt.plot(x,y)\n",
    "            else:\n",
    "                for geom in tractGeom[t].geoms:\n",
    "                    x,y = geom.exterior.xy\n",
    "                    plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 and tractGeom[t].centroid.y > 48.3:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            #isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 and vtdGeom[p].centroid.y > 48.3 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d7009c9a-d5ff-4570-855d-1fb439afc32c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD4CAYAAADo30HgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAABSvElEQVR4nO3dd3xddf348dfn3D2zd5qk6aK7pWEIlF1G2aKIAioqfF0/Qb/qV/Dr+iru71dwgYjiYKqAIrKRVSiF7j3TNM3eyd3jnM/vj3Ob5DZJm7aZ7ef5eNzmnvE553NPk/s+n3mElBJFURRFOUAb7wwoiqIoE4sKDIqiKEoaFRgURVGUNCowKIqiKGlUYFAURVHSWMc7A4PJzc2VFRUV450NRVGUSWPNmjVtUsq8kTjWhAwMFRUVrF69eryzoSiKMmkIIfaN1LFUVZKiKIqSRgUGRVEUJY0KDIqiKEoaFRgURVGUNCowKIqiKGlUYFAURVHSDDswCCEsQoh1QohnUssLhRArhRCbhBD/FEL4h0h3iRBihxBitxDiayOVcUVRFGV0HMk4htuAbcCBAPAA8GUp5etCiE8AXwG+0T+BEMIC/ApYBtQB7wkhnpZSbj3mnCvKEWhu/hfB0M7D7icQh93j8Ac59mMMtceBSfJLS27Abs8+fF4U5SgMKzAIIUqBy4C7gC+lVs8C3ki9fwl4gYMCA3AqsFtKWZ06zmPAVYAKDMqYqtl3L8HgtvHOxojRhI2Kik+PdzaU49RwSwx3A18FfP3WbQauBP4BfBCYMki6EmB/v+U64LTBTiCEuBW4FaCsrGyY2VKU4Vkw/zesXXcDiUQ7VUv+htc7a0SOO/wHXQ1nv8PvYxgx3n3vGvZU/5RkspvKytvRNMcw86Aow3PYNgYhxOVAi5RyzUGbPgF8TgixBjNgxAdLPsi6QX/7pZT3SymrpJRVeXkjMt2HovRyuUo4efGf0DQH27bfOWLHFUIM86UN42U57MticXNK1RMUF1/Hvtr7eXvlBdQ3PI6U+oh9JkUZTuPzmcCVQoga4DHgfCHEQ1LK7VLKi6SUS4BHgT2DpK0jvSRRCjQcY54V5ai4XGVUlH+Gnp717N//hyO4259YrFYvs0/6PosX/QmHo5Dt2+/k3feuprNz1XhnTTlOHDYwSCnvkFKWSikrgOuBf0spbxRC5AMIITTgv4H7Bkn+HjBDCDFVCGFPpX96xHKvKEeouPg6srPOYueu77J+/ceIRhvHO0tHLTv7TKqW/JV5c+8hmehi7bqPsH7DJ0kmA+OdNWWSO5ZxDB8WQuwEtmOWAh4EEEIUCyGeBZBSJoHPYzZMbwP+IqXccmxZVpSjZ7X6WLToQWbN/A7dPevYsPFTJJOh8c7WURNCUFBwOaef/hL5+ZfR3v4ar7+xiK3b/otoVBXOlaMjJmJxuqqqSqppt5XR1t7+Bus33My0yv+kouKz452dEdHesYLmpqdpbHoCh72A009/CavVM97ZUsaAEGKNlLJqJI6lRj4rJ6ycnLPx+xfR1vbvI0oXidTS3PwMjY1P0N7+OrF42yjl8MjlZJ9FRcVnAIjFm9H18DjnSJmMJuSDehRlLEgpicWaychYPOw0zc3/YvOWLwy6bc7sn1JYeDXiEAPcDCNGPN5OPN5GUg9h6FEMI4ZumD/N5SiGEQfM3kyaxYXLWYLTWYrTWYzVmnHIc3R0rux9v+Kt0ykqvJY5c3487M+oKCowKCesSKSGWKyRrMzPDLmPlBJdD5FM9pBIdB9y9PTWbV9m67Yvk+FfjEQCEikNwCCZDJBIdJFM9hxzvjXNjt2ej8ORj8NegN2Rh8NegMORh27ECAa2YrF40HWz7aSx6QkVGJQjogKDcsJqa3sVMKtc6uoeJp7oIJFoJx7vIB5vJRZrIR5vOaLqGJstB4vFA0KY02sIAWi43dOw2TKw23Kx282X1epF05xomgOLxfypaU4sFidmJz4AA10PEYnsJxKtIxZtIhZvIR5rIRZvIRjaSazjTXQ92JsHq9WHzzcPv28emVmnkZN91gheNeVEoAKDcsLKyFiMxeKlpuZXveusVj82WzYORz4+31wcjvNx2HOx2bKwWv3my+bHZs3Aas3AavVi9tgePZpmx2bLwu9fMOQ+uh4mFmtBCCtOZ8khq5oU5XBUYFBOWBkZi1l61kri8Q40zYHNlomm2cY7W0fFYnHjdleMdzaU44QKDMoJzWJx43K5xzsbijKhqO6qiqIoShoVGBRFUZQ0KjAoiqIoaVRgUBRFUdKowKAoiqKkUYFBURRFSaMCg6IoipJGBQZFURQljQoMiqIoShoVGBRFUZQ0KjAoiqIoaVRgUBRFUdKowKAoiqKkUYFBURRFSaMCg6IoipJGBQZFURQljQoMiqIoShoVGBRFUZQ0KjAoiqIoaVRgUBRFUdIMOzAIISxCiHVCiGdSy4uEEO8IIdYLIVYLIU4dIt1tQojNQogtQojbRyjfiqIoyig5khLDbcC2fss/Br4jpVwEfDO1nEYIMQ+4BTgVWAhcLoSYcdS5VRRFUUbdsAKDEKIUuAx4oN9qCfhT7zOAhkGSzgbekVKGpZRJ4HXgmqPPrqIoijLarMPc727gq4Cv37rbgReEED/FDDBnDJJuM3CXECIHiADLgdWDnUAIcStwK0BZWdkws6UoiqKMtMOWGIQQlwMtUso1B236DPBFKeUU4IvA7w5OK6XcBvwIeAl4HtgAJAc7j5TyfilllZSyKi8v78g+haIoijJihlOVdCZwpRCiBngMOF8I8RDwMeDJ1D5/xWxHGEBK+Tsp5clSyrOBDmDXMedaURRFGTWHDQxSyjuklKVSygrgeuDfUsobMdsUzkntdj5DfOELIfJTP8uA9wOPjkC+FUVRlFEy3DaGwdwC3COEsAJRUu0DQohi4AEp5fLUfk+k2hgSwOeklJ3HkmFFURRldB1RYJBSvga8lnq/AlgyyD4NmI3MB5aXHlMOFUVRlDGlRj4riqIoaVRgUBRFUdKowKAoiqKkUYFBURRFSaMCg6IoipJGBQZFURQljQoMiqIoShoVGBRFUZQ0KjAoiqIoaVRgUBRFUdKowKAoiqKkUYFBURRFSaMCg6IoipJGBQZFURQljQoMiqIoShoVGBRFUZQ0KjAoiqIoaVRgUBRFUdKowKAoiqKkUYFBURRFSaMCg6IoipJGBQZFURQljQoMiqIoShoVGBRFUZQ0KjAoiqIoaVRgUBRFUdKowKAoiqKkUYFBURRFSTPswCCEsAgh1gkhnkktLxJCvCOEWC+EWC2EOHWIdF8UQmwRQmwWQjwqhHCOVOYVRVGUkXckJYbbgG39ln8MfEdKuQj4Zmo5jRCiBPgCUCWlnAdYgOuPOreKoijKqBtWYBBClAKXAQ/0Wy0Bf+p9BtAwRHIr4BJCWAH3IfZTFEVRJgDrMPe7G/gq4Ou37nbgBSHETzEDzBkHJ5JS1qe21wIR4EUp5YuDnUAIcStwK0BZWdkws6UoiqKMtMOWGIQQlwMtUso1B236DPBFKeUU4IvA7wZJmwVcBUwFigGPEOLGwc4jpbxfSlklpazKy8s7wo+hKIqijJThVCWdCVwphKgBHgPOF0I8BHwMeDK1z1+BwRqfLwT2SilbpZSJ1P4DShaKoigHk4ZE74khdTneWTnhHLYqSUp5B3AHgBDiXODLUsobhRDbgHOA14DzgV2DJK8FThdCuDGrki4AVo9ExhVFOb4FXq+j54WatHW2Yg/CbsHis2Px2xGOA+8dWDIdWHOcCLsFoYkhjyulxAgmSHZE0XviWLOdaE4LmsuK5raN8qeaHIbbxjCYW4B7Uo3KUVLtA0KIYuABKeVyKeUqIcTfgLVAElgH3H+MeVYU5QTQGNqDU0osou9rKtEQGlZaYbcgdQOhCYTDgnBY0OwWhN1Csj2CEUwMms55UjY5H5uDEEMHlhPBEQUGKeVrmCUEpJQrgCWD7NMALO+3/C3gW8eSSUVRTjydsSbeqnkIu+aiwFWO35aDx5rBVN/8w6aVcR0EoAlkTEfGdAzM6imEwJLpQNg1jEgSI9AXJGJ7u8HA7Fh/AhNSTrz6u6qqKrl6tapxUpQTXSIeo6NuPx0NdUgpiQYDuDw+SrNnY7M70HtiGFEdGU2iBxNEd3aid0SHd3CrwJbnxlbowVrgxjk9E3up7/DpJighxBopZdVIHOtYqpIURVFGlc3uoKByOgWV048onTQkMmEg4zoyrmPE9NR7A81tRfPasPgcCMuJXWU0FBUYFEU57hxoW8BxgtcJHSU1iZ6iKIqSRgUGRVEUJY0KDIqiKEoaFRgURVGUNCowKIqiKGlUYFAURVHSqMCgKIqipFGBQVEURUmjAoOiKIqSRgUGRVEUJY0KDIqiKEoaFRgURVGUNCowKIqiKGlUYFAURVHSqGm3FWUCa67p4W8/HPjQKl+2k0DqgTQf/tZpZBd5xjprynFMlRgUZQLbv7V90PWBfk8p2/Dv/Ud8XD0YYttJs9l20mx2nX8+Rmh4z1JWTgyqxKAoE1jV8qnMOKWAlpoAFpuGlBI9YaBZNOwuC06Pjfxy/xEfN1Ff3/s+2dBIdOdO3IsXj2TWlUlMBQZFmeAy8txk5LlH9JjOWTM5afMmolu2YCstxZqTM6LHVyY3FRgU5QTQFmljQ8sGOmOd6IaOLnUMaaBbdcJ1b/O7537H8qnLOb3odOblzqPMXzbeWVbGkQoMijLBxfQYNs2GJobXJJg0ktQF6ljXso61LWtZ17KOfT37Dpvuqd1P8dTup7Brdu466y4umXrJsWZdmaRUYFCUCWhHxw4e3PIgK+pX0B3rBsCqWXFanDgsDpxWJ3aLvXfZYXUQTUZpDDXSFmnDkAYAGY4MFucv5toZ17I4fzGFnkKsmhWLsKAJDatmRRNm20U4GaYj2sGNz97I3/f8XQWGE5gKDIoywbxQ8wJ3vHkHTouT88vOp9xfTlImiSVjxPQYUT1KLJn6qcfM9ckYDouD04tOp8hTRLG3mAW5C6jMrBx2ScNtc9McbiaSjHBh2YWj/CnHViIWpXnrLvLKKrH7XQjb6HTIlFKip3qMWTIcGJEkmteGEGJUzjdaVGBQlAmiO9bNfRvu4+FtD7MwbyE/P//nZDmzxjQPq5vMMRNFnqIxPe9o+/3n/4PLcj5FK2vT1lsL3NhLfViznViyHGgOK1gEMmEgEzoybiBjOkYkiRFJpH4mEVYNYREgQUowIgn0njh6e3TAua15LjKvmIZz5tj+Xx4LIaUc7zwMUFVVJVevHjioR1GON8F4kFWNq3ip9iX+XftvIskIH5r1Ib5c9WWcVueY52d/YD83P38zbZE2PnzSh7l1wa1jHpxGQ0dDHRvufooZ2qKjO4AGmsuG5rYinFZIGEhDQqogoLmtWPwOjHCCRFMYW5EHR4WfnhfNth3htFDy7TNG5sMMQQixRkpZNSLHGm5gEEJYgNVAvZTyciHEIuA+wAkkgc9KKd89KM0s4PF+qyqBb0op7z7UuVRgUI43cT1OW6SNlnALOzp2sKltE5vaNrG3ey8SSYYjgwvKLuCG2TcwM2vmuOa1J97Dz9b8jCd3PYnb6uaT8z/JR+d8FLvFPq75GilSSpItYeK1AYxIkujOTmK7u9B8dnI/PhcMCVYNza4hbBaEQ0PYLUdVHSSTBl3/3EN4fSsl35k8geFIqpJuA7YBB0bT/Bj4jpTyOSHE8tTyuf0TSCl3AIugN7DUA08dW5YVZeJIGAnaI+20hltpibSYP8MttEZa09Z1xbrS0mU7s1mQu4DlU5ezOH8xJxecjFUbv5rd2p5avvTal9jVtQuLsFDqK8VldRFMBLln7T347X6um3XduOVvJAkhsBV4sBWY04j4zi6l4a53MAJxwutayLy8cuTOZTXbMmRcp+n/+t/sHhRkBCAl7oX5+C8Y/67Cw/pNFEKUApcBdwFfSq2W9AWJDKDhMIe5ANgjpTx8vzlFmaD+sPkP/O+a/x3WvmW+Msr95SzOW0yeO498dz65rlxmZM6g0FM4oRokN7RuYEfnDgAMabC3ey+nFZ7GkoIlVGZWcu6Uc8c3g8egozHEq3/eTtncbKourUBo5nVPtkWI7uwksq0dI5AAwF7qHfHzuxfmYUSS5jdmSrw+2NtI3V9oTfPkCQzA3cBXAV+/dbcDLwghfoo559LhyknXA48OtVEIcStwK0BZ2fhfGEUZzNPVTw9739pALbWBWuyaHY/Ng9fuJdeVS64rtzdI5LnyyHPlkes232c6MsclYCyfuhyAO1fc2btuVdMqrJqVj839GA6LY8zzNFJ2vNNEU3U3TdXdWGwa8xfk0nx3XyO0JceJ/8Iy3KcUYs0Y+c/pqMzEUZmZtq790e1EOqKgCbO0IICkxFYwsiPcj9Zh2xiEEJcDy6WUnxVCnAt8OdXG8HPgdSnlE0KI64BbpZSD9nETQtgxSxRzpZTNh8uUamNQJqqYHqM+WE9CT5AwEsT1OHEjTlyP961LLUeSEUKJEOFkmHAiTE+sh7ZoG63hVtoibQQTwQHHd1ldLMhbwJKCJVQVVDE/d/6YN0KHE2HeaXyH95re46FtD3H7ybfzyfmfHNM8jCTDkGx7q4Hd7zZx4fIKep6pRm+PYq/wk/2BmVhzXWOSDyklMqqjB+N0/m0X8X09FN15Ghb/yLTdjHUbw5nAlal2BCfgF0I8BFyB2e4A8FfggUMc41Jg7XCCwliQUoKkt0g5lGRCJxZK4vTZsFjURLQKOCwOKjNGpg46nAjTFmkz2yMirbSF26gN1LKuZR33rr8XicSqWZmfO5/Ti07njOIzmJ87H4tmGZHzD6U53My2jm08X/M8MLG7rkopCa1qJNkexTEtE3SJEdeRkSRGuK97aV57lMzuGJ1/3IpwWsi6biaekwtGJ09Jg+ieLuK1ARL1QfRAHCMQRw8lQE+/EU92RkcsMIykI+quelCJYRvwGSnla0KIC4AfSymXDJHuMeAFKeWDwznP0ZYYNryynxV/3UXpSVlY7RaScZ14VCcRTRKPJIlHdfSEgWGYn1mzCmx2CzaHBavdgsWqmWliZppk3Og9ts1pwe6wYHNasTvNn74cJznFHrKLPeSW+nBPwP9gZXLqifewvmU9a5rXsLppNRvbNvZuW/WRVbhtI1vlUBeo4/ma53mh5gW2d2xHIDij5Ayun3U955SeM6HaQ/pr+c0G4nt7htwuHBY0lxVLpgNbkQfnjCwc0zPR7CMXXKWUZhBoDJJoCBHZ3IYRToIAa74ba5YTzWPD4rWheW1oXjsWrw1rrgtr1siVBserV9LBbgHuEUJYgSip9gEhRDHwgJRyeWrZDSwD/uMY83pY7zxdDUDd9k5ySr3Y7BpOtxVfthO7y4LdYcVi19AsAiEEekInETNIxHWSMZ1kwsDmsPQGgVB3nPodnVQszMVq00hEzUDTXh+kZV9gwPmFJrj5R2fi8pkBQg+GiG7dgoxG0TweHLNmoXk8JJuaiO/dixGNElr5DpaMDPyXXoI1Px/N652wf4TK2PHb/ZxdejZnl54NmAPPbn7hZsBsuzgp+6RjOn7CSLC9fTtrW9ayon4F7zS+A8CCvAV89ZSvclH5RRR4RueOeiRlXTU9rb0AIP/zi7BkOtBcVsQolfRlwiDRFCJW0010WwexanPaEmHXcJ6UjfvkAhyVGSMagMbSEQUGKeVrwGup9yuAASUEKWUDsLzfchgYkzl9y+ZkU72uFYB5Z5dQuSgPl9d22CqjI/XAf74x6HppSKKhBC6fnZ7nnqP+i18adL/BtP3yl73vNbebiscfwzFjxjHnVZn82iJtrGxc2bvssh55nXhXtIutHVtZ37Ketc1r2di2kUgyAsAU3xQ+t+hzXF55OaW+0hHL90iSUmIEEyQ7oiQ7oujtEfN9exRh05AJAywC3zml2Et9hz/gEZxX74yZpYGWiPmzMUSyLdLby8iS7STjskpcC3Kx+O3HxY3dcTUlxoyqgt7A8PojO3j9kR1YbBr+XBdOj5XLP78Qu/PYP/KMqgI2v24+6GTGKQUsOL+UjFwXhiFJxnWSCZ22+35zyGPYKyrwnHEG8Zq9hN5embbNCIfRPOmPauxpi7B/WweBjiixUBJPpoOMPBels7NweVUV1vEmYSR4ed/LPL7jcdY2m3fEF5ZdyOcXf55yf/mA/aWUtEZa2du9l5ruGvb27KUx2EhrpJXaQG3vRHwCwazsWVwz/RpOLjiZxfmLyXfnj+lnOxQ9lCC+r8f88u+KmT87zCAg+1XtIsDit2PJduE5pRBHZQb2qRlYPLaRyUd3jMCKesLrWzEC8d71liwHtiIvrvm52ArN0c0W/+TtsTWU43JKjHBPnOaaHgLtUda+sI9QVwwwn5N73ddPweG2HnNUf+tvu1j/8uCPVNQ0gdNrpXCKi/JZfvRAgM7/+xG2ZBhrIowz1oFVjw1M5/FQ+eyz2AryiYYS1G3vpGFnJ/u3d9LVHAbM6iq7y0IslOxNN3dpMWd9cAbWSVpsVdI9X/M8v1j7C2oDtZT5yrh06qWcVXIWDouDhmAD9cF6GkINNAYb6Yp10RXrojncTCjR93hOl9VFsaeYXHdu73iKGVkzmJ87H5995O6oj5ZMGMT39xCvC5JoCZNsjZBsDZt18ynCpmHJdmI98Mpxmcs5TqyZzhGfCE8Pxont6Sa6o4PwhlaQEtfsHBzTM7GVeLHlu9FG4MZytIzLlBhjaSS7q+7f1sHT96xPW2d1WNA0gTQk3iwHvhwXhZV+Zp1WiH+YXdeklHS3RmitDfDiA1uOOn8eguTuewuLHsGiJ5jx3a/gKszhiR+v6d1nypxsCqb6mbGkgMwCF5pFIxHXadrTzat/3k6gI4rNYSG/3EdGnousIg+GLqlclEfmBOkXrQxPW6SN8/5yXu+yJrTeKbT789g8FHmKyHJmkenIJNeVS4W/gqkZU5maMZUCd8GEqtKQUhJe3Uyspge9M0p8f8Cs/gE0rw1rnhtbvgtrnht7sRdboRvhOvYbuMPmS5cE36onvK6FRKMZWIXTgntBHr5zp2DNHvv5qo6WCgxHwdANmqq7ad4bINQd48DnDnbG6G6J0F5v9im/4gsLKZsz/CaRruYwD3/rnRHNa38Oj5VrvnQyVrtGJJAgu9iTVh0mpeTdf+5l9bM1A9L6c51c+61FrG9Zj1Wz8o89/2BdyzrOLD6T04tOp8xfRkVGBTZtZIrfysh4seZFXtv/Gn6HH7fVjd1iDpAr9hRT7DVffrt/Qn3xH05kcxvtD20DwF7mw1bgwXlSNvYK/4hV/xyNzqd2EVrVhL3Cj3NWFo5pmdhLfObMqZOMCgyjoGZjG//6tdkl0J/rxJfjJLvYS06xh5wS74Av5P6kIQl0RulujtDZHKarJUygPUp3S5jOJrMKSNME+RV+EnEdI2lgTXWT9WY50DRBOJCgpy3SW2U0FKEJ8sp8FE3LIK/MR8FUPy8+sIXWWrOX1EmnF2KxaewL1/BUzyPsyV13yOM5LA5mZc/CZXWxtGQpFf4KM2D4KybVF8+Y6KoFBGROGe+cTBrBlQ10PVNtTkwnIeOKSnxnlox3tgDz77b+zhUAlHzvzN55jSYrFRhGSev+ALvebSbYFaOnLUJ7Q4hkTO/dbgYLD9mFHrKKPGQXecgqch+yQTsaSrD59Tr2bW4nEdPRk5JYOEEkNTcLgDvDjsNlxZ3hwJvlwO6yYrEI4jEdu9NKQYWfRCyJw22jpaaHht1dNO7u7k0/ZU42bp+dPWtbSCYM8st9tC1by6+3/HJAfpaVL6PAXYDdYieajNIZ66S6q7p3npz+PjTrQ/z36f99tJfz+PPtDPPnF9ZB9shNtHa8kEmDZFuERJPZayfZGSO8xhzT6l9WjruqYFSmnDhSeiBO4M06wmtbMIIJ0KDojtOw+CZ3Jw4VGMaINCQ97VHa64N0NATpaAzT0RCiszmEkey7bv5cJzklXrxZThweK06PDafHhsNtNev8Cz0Djh2PJulsDLP5zXpz0J1u0NEQIhHXSUR1Yv0a4T7036eQW+qDSBf8bC7Eg7wx7z42vdzXz3zJJeU07OqicY8ZMC65dR7Zc2zs7trN3u69/HbTb2kKNQ36OTMdmczMmkmxt5i/7/572rYpvilkObJYlL+IebnzmJszlym+KSdmaeK7+aDH4BttYFHVbwDhja10PLIdzWszJ4rrN7JX89hwVGbgX1aOLX/827r0YJzA63WE3mlE6gau2Tm4FubhnJGF5pq4jcrDpQLDODN0g562KB2NIToaQrTXB2mrCxIJxNO+0A/IKnSTWeDGYtUwDIk/x4lmETi9drKLPeQUe/Fm9d1JGYYkEojzh/96C4Dr7jyFvDIfhDvgx1MB+EriVv6RPJfbus3Gck0TvSO6S2ZlceUXFqIdNLhHN3QaQg1IKYkkIzSHm6kL1LGjcwdb27eyu3M3STkw/4P50pIvkTSSZDmzmJY5jRmZM/DaR35mSmVikUmD6O4u4rU9JBpDRLd1AGYDsqeqAFuhB1uhB2uua8JUzejBOIE36wm93YBMGrgX5+M/v2zM5kgaKyowTGCGIYmHk0RDCaKhBA27umiq7qa7NYKhS4SAnvYo0pAY/e6u8sp8TF+Sj5402P5OEz2tkd5tn/q/pTjcqTtUKZFS8vL2VlbsauVDp5Qxp9ic/VzXDYykxOY4um6rcT3Ors5dbGnfwtb2rbxR9watkdZhpXVb3bz8wZcJGA6ea+tmSzDCD2eWYtcmxpeDcuSkIUnUB4nX9hBvCKF3x8zeRDHdnO4hz429xIvntEIcFRnjnd1B6YE4TT95Dxk3cC/Kw3dBGba88S+9jIaJMiWGMghzDIMNp9f8Ii+sHPgHcyAYx0JJOhqDNNcE2PluEyuf2gNATomHBeeV4vLZWXxxWfoEfsKczmPZnAKWzUmfssBi0bAcw1AGu8XO3Ny5zM2d27tub/deWsOtvQ+eD8QDvLzvZWp6auiMdmLVrHTFujin9BzcVjczXt/Um/byvEzOz/EPdiplApIJg1hNN/F9qdJAdTcyYpYgNa8NS5YT98I8XHNzsE+dHNM9aE4LmtOKHo+TeeU0NLeqAhwOFRjGwYH6eafXRvGMLIpnZLF4WRnhnjiaRYzIALyRcqBffH/vn/F+EoluunvWIY0EOTnnoGlmw91PZpVyb20rc70uzsxSVUuTRfdL+wi8UmsuCLBkOXHNycE5I9McUTzJpnqQhiRe0014Yxt6jzlyWQ8mVGAYJhUYJpCRnp3V0HUSsRjJeIxELIY0zOVAextCE5TNW4TVduR/KIHANt597/LeZYvFy5lnvMb+/Z1oK1dydX094XCYH1sszJkzh3POOYfc3NyR/GgjRuqSZHsEPRAnXhtA2DQ8pxSgOU6MPw29J0b3C/t6ew9lXz8L50nZE3qE7+GE1jbT/exejGACYdNwzcvBfUrhhGgAnywm7/++ckibX3uZF+69+7D7Fc2YhcPjxeZwkFVYTP7UaXizc/Hl5OLLzkEM0kZgsThxu6cTDu8GQEqdeLyLv/zlL0SjfY8r1HWdTZs2sWnTJq6//npOOunYZgQdKUZcJ/hWA7HqLuL7Asi4nra9+5lqhN1C4VeqJn0XxkPRA3Eav/8uALYSLzk3zh7RaaDHmtQlwRX1dD+/F3uZH++V03DOykY7yja3E5kKDMcpT0bmsPZr3DVw/MIBVpudjIJC3P4MMouKySwowuXz487IpMj9Q+KE0Kw2hBB01oa4/tr30xEIEggECAZ6iEZj7Nq9G5fLhdc7caqVOh7fQXRL+yH3kXE97Rm9x6PGu1YB5kjk/M8uGvXzSUOCYNSqpLr/VU3w7Qacc3LIvn7WpGgDmahUYDhOTV1cxX8+/gzRYJBgRxvRUJBoKEQ0GCDS002ws4NgRzvBjnYCHe2EOjsw9PSuqslEnPa6WtqB/Vs3DX6igwhNQxp9c/vYhYbmdPJmez1F02fgzysgI7+QzIIiMguLsFjH/lfQszgfdImwCPRwEgyJ5rKaDawZDlwnZWOfMv4TzY0GqRt0/2uvOeYgJe/WBSN+HiOuk2wO0/7odvSOKNYcJ8kOszRp8Tuwl/uwl/txlPuxFXtHZGp8I5JEOCzk3DR7UrWHTEQqMBznnF4vzsPcrf/mC69h8+kgI0ijC0Nvo3i6wO2L09nYQOu+agy9r7rFnZFJdnEpWUXF5JZVUDB1OolYlM7GeoKdHdidLjSLBc1iJRoKEA0GaN67hw0vPkcy0TeFsWaxkllYRF75VOYsPY/Kk08ZtevQn2teLq55E7PNYzQZcZ3m/1uDnppt2FbowV1VMOLjDToe3UJ4Q0faukRLD5q9Hu/ZZ5NsjxDfFyCysc3MR6mX7A/ORHPbzCeuHeWdvsVvR8Z0ZFxHnCBtRKNFXT0Ff66LjoYQCDdCc6NZi2nZD5+773wAEtEojbt30rhrO23799FWW0PDzm3UbdsMmKUEh8dLdlEJGfkFZvtETh6+3Fx8OfPx5eTi9Jp34JGebrqam+hqajBLI/V17F33HjvefoOM/AKyi0vx5eThycrGm52Dy+/H4XJTOH0mdufxNSBpLMiETvDtRnpe3Y+MJyFVmCv5wVkjelctEwaBt+pBl2lBYWNpCz3v7mPh6gfBSFJy10d7tyW7Y4TebSLwSi3NPzOfOSFsGnm3LjjiEpsRTRJa04y9wn/CdBwYTWqAmwKkBjOl6tXj0STewzRC6skEnQ31tO7fR/v+WiKBbtrr9hNobyPY0ZZWwgCw2h34cnLxZGXh9PhwuD14s3PIyC8gHgnz1uMPkYhFhzhbn/IFi3n/176NdiwDNo5j0pAkmsPEa3uI7TUfOyljOpYcJxafHXupD9+5pVhG+OFOsZpuWu/bmLbu/53h5ckrFhGJJQn/8UEy338N1ry8AWnjDUES9UGMmE7PCzXIpIF9ig/XvFw8pxUe8oteGpLgm/UE32lA746R/9lFI/oEt8lEDXBTRpzQRO9kgPZhzBtjsdrILasgt6xiwDZpGIS6uwi0txJobyPQ1tb7PtTVQVdTA9FQkGBHe7/jWXF6fRh60mzgzswi2N5OoD195HVnYz2GrqvA0I9MGvS8Uku8LkC8NjUyGXOuItf8XNwL83BMyzyqenw9lCC8ugl7mR97mW/IZyj3H/lsYOBZXMDvYhrdz1RjLXDjPe+DaL7Bv7DtxV7sxWZ1p/OkbCLrW4js6KT72b0EXtuP98wSPFUFyIRBZKv5OyMTBuGNrSRTsxHbij1kXjX9hA0KI02VGJRxE4+EiQaDIMCbnYOmDfyyT8Si9LS2AAKX34/LN7meQzAWWu7dQHxfDwCe04uwl/lwlPuxZDuP+Vo1/d8aki19U8H7l5Xjv6Bs0H31nhjtj+8g0RRCWDSEJnrbMw4ouP1kbINMKjmYWG0PgX/vJ7q9AwRD9hLznlVCxqVTJ+UzFEaSKjEchwKvvUbHg3/AedJJ5P/XVwcdPzDaIskI3bFugvEgbdE2shxZzMiagSZGJy92lxu769CDjmwOJzmlg38RKaYDQcHit5N19fQRPbbnlEK6/1XduxxLnWswFr+D/Fv6ejiF17XQ8Xh6d2g9lGC4QyodZX4cH59LojlEeEMr0W0deE4rwr0oD2HVJswkfccjFRgmiKZvfJNkayvhVavIuPoqnLNn0x1J8Ni7texoDvA/V83DezSNal21cPd88/2XtoO/KG3z/sB+PvrcR2mLtB3RYS+tuJQfnf2jQe9IpZT0xFN3sDYPmtAQiEH3jSQjrGleQ5GniFxXbu+MrVJKqruraYu0kTSSbG3fSlIm+czCz4xaoBprwViSQDRBrteBbYgqmsPpX+LP+/TCEcmXEU3S8eh29HASa056W5MQkGgND2siuliNOQW8c04O7oW5RHd2YQTi6MH4EbVx2Ao8ZFzkIeOiiiP6HMrRU4Fhgqh87jm6/vIXep55BtuUKYTjSZZ89yWSqam0n1xbz8feV45F07hyUTGLpmSmpdelZENPmDKXg1x7v//WaN8DfQg2DQgMgXjgiIMCwHM1z+G2uQklQjgsDvLd+TgsDtqj7axuXs2uzl29+7qsLgSCcn85HpuHPFcehZ5CYnqMR7Y/MuDYVmHFqlmJ6gMbo+/bcB9LCpYwO3s2ua5cSrwlXFRx0aQIFtGEztraTva0BHlyXT3rart6t/3iw4u5YmEx2xp7WFfbxdxiPzMLfLgO03Wzf7BNdkZH5BnFRkwnuqPTPGZzCFuhG/+F5ebAwB2dRHesGVavJmuO2YssurWdaKpt4MDUG96lJeaznUu8aqqKCUi1MUxA3ZEE3//XNh5fvR+rJphd5MdqEb1fJBU5bl77ivnAeMNIUlf/Zz5VW8bGuPms6u0n69ht2eh6hO6e9UTCNQRDO6mc+gUyMk6mKdREdXc1G1s38m7Tu6xvWU/C6HuiXJmvDK/dS5GniEJPITbNRmuklZf3vUxMT/WB12z47X68di+hRIiOaAeGNPDavEzLnMbZpWcTjAcJJ8PE9TgSSXuknVAiRGuklaZQEwkjQZmvjMsqLyPXlUskGcEiLLRGWkkYCWZnzybPnYfD4iDDnsHfdv2NWDLG9s7t7OzY2Rs4/njJHzm54OQx/B86Op/8w3u8sr2ld/mLF87kZy/v7F0+a3ouK3b3BWmP3cLJ5VncfuEMlpRnD3nc6I4O2h7cAkDBl5Yc8xet3hOn8furcM3NIeemOb3r+z8KM+PySnxnHfoRncmOKF1P7yHZGUUmDGTCwJrjRO+MoocSkHrYlXNODrkfnXPIYymHp57HcByq74rw0pYm3t7TzsrqdkKxJLcsreQrF8/Cmqpm+PM7+/jG382xA9XfX46mCbp7NrB69fupYwr/Je7mXPkSt3DfkOeJT/0lX33jq73Ls7Nnc2rhqZxadCpLCpbgsQ3dMJg0knTFuvDYPDgt6Q2bhjRIGknslrGZW0g3dO7feD+/3vBrSrwlXFxxMddMv4YSbwm2Cfp0tUdW1fLTF3fQETIH+f3uY1XUdUb41tNbKPA7KMxwgZR8amklFk3w1u42nl7fgBDw9OfPoiJ36P+bzid2EXqviZyb5uCamzPsPBlxnfj+AImmEHpnjGRbhOj2Dv5eYuN785xckuvnD/P7HmOqhxI0/WQ1MprEOTsb33lTcJQd+dTq0V2dtP1uc++yf1k5tlIv3c9U46jMIPOKaaoN4QipwHCckFKyrz3MT17cwb82NgIwJdvF6VNz+Oj7KphfmkFDV4TnNzfx9p42Vu5pJ5Sa8G37dy/BabMgpaSjYwXB4Fa6u9fRE9hMLNY46PmmVX6FgOdMPvLsR3rXZTuzKXAXkOfO48KyC7l6+tWTptdPXI/zp61/4sWaF9nWsQ2AaRnT+PvVfx/fjB3GMxsb+Pwj6wC46fRyXHYLdZ1hnt3UhMOqMavQh8OqEU0YTM/38tS6egD++fmzmF86+ANxonu6aPutOW1J1gdnYs1xYst3p00zbYQTxBtDJJpCJA78bAhBqroSqwZJgx4rnH9BX7fPfecswNGvM4QRSRJ8u4HAinpkJIm9zIc1340RiONfVj6sLqNGXKf1NxtJ1AfNFQK0TAdGp1kizf30ApwT9OE/E5UKDJOQlJL6rghPrq3ngTercdgsSAltQfMP4aRCH7+5aQnlOeZd4a9e3c1PXujr0ZHrtXPR3ELOm5XPGdNy8AyjIVpKSWuokV2dO6gNNpIwklg1K5rQyHPl0Rpp5bm9z7G2ZW1vmheufYFib3Hv8ntN7/HNt75JXbCOcn8555edz8fnfpxs59BVG2MtmoxyysPmdBo2zcbam9YeJsX4e3tPG794ZTeb6ruJJXUSqaf5nTo1m0hcx2W3UNMWoiXQ193zza+ex5TsoauJIts76PzbTvMB9wDCnPZC6gZGONm3HtA8VmxFZh2/Y2oG9mIvms9mziFl1VjRGaA1nuTcbB9ZtsF/14xYktCqJsJrW0g0hXrXF3/njMPOaNr1TDXBFfW9y87Z2XjeV0TXGzW01u3lpW0PAjDjtDO48kt3HvJYimlcAoMQwgKsBuqllJcLIRYB9wFOIAl8Vkr57iDpMoEHgHmYPZE/IaVceahzHU+BIaEb3PnkJl7Z3tJbhbCgNIMZ+b7e9xfMzqc0q+8PPp40mPnfzw041so7zqco49DTQlR3V/NOwztsad/CprZN7O3eO6x8VvgruGLaFdwy/xaEEHTHunl8x+P8Yt0vBt3/oeUPsSB3wbiXLhqCDVz8xMW9yyuuX0GGo+9OM5FIIKXEbp+402dH4jqN3REyXDZyvH3P/tYNyZaGbpp7YpTnuJlZcPg7cWlIkm0R9M4osZoe4vVBNIf5FDNrrgtbkQeZE2fz3s8SCu0mM7OKuXN+hs12bE/ak7pB5992EV7XAiLV1nGInkvxugCtD2xGRvsm85NWyV92/RjDasOwObDEIghD5z8ff+aY8naiGK/A8CWgCvCnAsOLwM+klM8JIZYDX5VSnjtIuj8Cb0opHxBC2AG3lLLrUOc6XgLDntYgX3p8PRvqurlwdj7nzMxjXkkG80oy0ronJnSDv66u49297WxvCrC7JdjbG6m/ZXMKmJLlJtNtI8ttI9NtZ0FpRm8poznUzIV/u7B3/3NKz2FR/iLm5c6jzFeGx+bBkAZb27fy6Zc/DcDnFn2Oyysvp9RXyo6OHTy6/VHebnibxlBfdVSeK4+LKy5mW8c21jSvScvT9MzpPLz8Ydy2se1ZIqVkwZ/6+sxbNSvrblqXts+ePXv485//DMDtt99OZmbmWGZxQorF21ix4rS0dYsX/Yns7DOP+dhSSjr/stMMDkDRneZ5Dky3PdhT4JJdMToe3058r9m9+ZmmP9JUXgypXmYXnFbF0ksvRzm8MR/gJoQoBS4D7gK+lFotgQO3GRlAwyDp/MDZwMcBpJRxIH7wfsebeNLgkVX7+PY/twJw94cWcfXioXtw3P9GdW+10bmz8jjvpHxKs1z8eeU+tjcFAPA5rGxr7GHlnnaCseSAY9x39gwqV+yDWX3ryvxl3Dz3ZtoibaxsXElbpI1wIkyWM4snr3ySyoxKLKnRxrqh84F/fgCAUwtP5QMzP8D6lvV854zvkOfum98mkozw9O6n+d6q7wGwu2s3MT02JoFBSsnbDW+zs3MnG1v75uW557x7WFq6dMD+27Zt630/kZ4HMdr0YJxEYwh7pZ+2/fvILi7FmioxRSO1A/bPyjrjkMczDIOWlhaEEPT09BAMBtm3bx8tLS0Eg0EMw8DhcKDrOl1dXeTbM7govoDG769KO46wWxB2Dc1hQTgsWDIc2Io8vUEB4LpPfoO32texZv16AGafcvoxXg3laAyrxCCE+BvwA8AHfDlVYpgNvIA5WF0DzpBS7jso3SLgfmArsBBYA9wmpQxxECHErcCtAGVlZUv27dt38C4TXk80wa9f3cNT6+po7olxclkmn1payfL5RYPuH03o7G0Lcek9b6atf//JJTR1R3l7TzsXzi7g6sXFdIbivLi1mU313XSF++qKha0dW+Zq5uZFmdopWVi5hJ92/GpY+RUIclw5xJIxgokgMjXnwL0X3stZJWeZnyneQ21PLbrUebHmRZ7a/RSBeIAcZw7Lypfx0bkfZYpvytFcriO2t3svV/79ygHrPzTrQ9g0GzaLjQJ3AYWeQgo9hRS4CrDoFjK8GZNinMNIqfv6CtAl+4JbeKe1rxqmZNYcln/hK3izs4hG99PTs5H8/MvQtIH3h6+++ipbt24lGAwSjUY5+HvC5XJRVFSEz+dD0zTi8Tj79u0jGDQbk0+ZsoCl009Bc1oRmkDGdZIdZrdVI6ZjRBJ0toRxdMawpEoR2dfPwjU3F2E7cf6vRtKYViUJIS4HlkspPyuEOJe+wPBz4HUp5RNCiOuAW6WUFx6Utgp4BzhTSrlKCHEP0COl/MahzjkZq5L2tAa54beraAlEOXdWPtdVTeGSeYVD7v+P9fXc9tj6IbeXZrkoynAys8DHP9Y3EIwlqczzcNrUHKbleSjNclGc6eKezV/l3eZDNtmgCY3vnfk9lpUvwyIs5hiG2ndZ1bUBh9WBz+7DZ/eR58pjZtZMFuYtpD3aznl/OW/AsVzCzjW+pSxyTkMg0qevObi9QcqB6/pvTkt7iG29hxesCG3kvfA2mpIdg+xxaKe75+KxuLgm4xxOzV+CY8YMNOckfpSllEjkgKAXeq+Jzid28UL9g3TFWwakuy67jCn3/nrI4yaTSb73PbNEWFlZSXZ2Nj6fD6fT2fs0vpKSEiwWC9aDHrT0zDPP0P9vd+nSpVxwwQUDzvH2k7tZ96JZejlneTmzLyjD4pmY3Ywni7GuSjoTuDLVjuAE/EKIh4ArgNtS+/wVs4H5YHVAnZTyQJnyb8DXji3LE9O+9hBNPVF+8oEFfLBq4B30vvYQK3a30dwdZd3+Lt7cZQ5kuv6UKVwwu4C2YIyNdV08+u5+LptfxK9uOJlL7n6Dh1fV4rZbuPtDi7hqUXFaHW1ntJP+MyksylvE+tb1A859/7L7Oa2or17Z/c1fMO/lV5jvcGArLMQxcybWvFywdJNsWsH+aJRWdxJtERgH3bxFZJxHel7hkZ5Xjul6jYd3wuYgsFcCq/nL55KgadgrK9mbW0711R/FW1TIY+/VsrHOHC1enuPG57RyxYJibj27ctwb2g+QUvLbTb9N6xhg02w8eMmDLMxbiOeUQtbuep6uvelBwR2LU1XdRDC+B/QEpMZ7GIakIdX47XPasFqtXHDBBbzyyitUV1dTXV3NULKyssjOzqanp4dYLEZPT/pcSi0tAwMTQF5ZX0N6wmlTQWGCOWxgkFLeAdwB0K/EcKMQYhtwDvAacD6wa5C0TUKI/UKIWVLKHcAFmNVKx525xWZPmN2twUG3f+IP77Gn1axBK81yccXCYn74/vm93U7D8STPbW4CYHq+WR/+y48s5mcv7eJfmxq5/fH1BKIJbnpfRe8x/1X9L1Y29pUWDgSF2+yXYHnyRSrbrRTGXYh7byf083vwnHoqXU88SfBl80s98wMfINnSTGzPHkLvvguJBNaiIjS3m6yWOM/o52C8bzFNUzOwav3/cNPv5wXCLB0cTEpzW9qqg9IeYtbMtJTD7Vad2q//eaWUGBi0JboIG1Hmu6eT9wsrsW3baHv+JUreeYU/Jwt5vXRx2qH2tZuzim6u7+GV7S1U5no4/6R8ls0pGNcgEdNjA3qLJYwE1lSV0JPNnXwhdw4v3/E/eOw281nddid6WyvWJ6/FEtgH382lIjpwOpK9P1iOEIKlS5cyd+5campq6OzsJBwOk5+fj81mQ9d1YrEYuq5TW1tLJBIhJycHh8OB3282OxYUFDBlyhQyMgYfizCjqoAZVQXouoE2Ao/1VEbWscyVdAtwjxDCCkRJtQ8IIYqBB6SUy1P7/T/g4VSPpGrg5mM454SV73Nw6tRsfvtGNa9tb2VusZ85xX4y3XYEUJbtZk9rKG06i/7ue72aN3a2Uprl4uYzKwCYnu/jVzecTOmz2/jNG9V84x9buO6UKTisZoPxFdOuQNQ20PLnP2AIyA7A4j0SR/JAvXIcA/PLree55/CceiquRQvR3G6McJjOhx/GWlyMxevFOWsWvmXLyLjqSiz+9K6L5aN10cbbsmW0LDkL7ZM3cNNZ0/jhh88nnjTI8TpwWjV0KfnH+gbe2NnKruYgj723n8fe20+u18GS8kyWlGexpDyLucUZOG1j93wIp9XJny79E7u7djPVP5XKzEqyHFm9weqzW/eBzc7LWUV8vrygN53V50Ve9VN46Br+I377oMfuH/Cys7PJzh7d8SqWo5w8UBldaoDbCOqOJPjdir1squtia2MPzT19g5MsmuDM6bl87txpnFY5cMqC/R1hbvnTarY3BXDbLdx0ejlziv28ur2F13e20plqcL7vxiVpbRehlSupvfkTAGTdeCPOuXORyQSJunraf/Ob3v1sU6aQ+5nPIGw2bMVFhHfu4M///B4l7bAolIM1N5fY9u1oPh/TXnwBa1bWaF2mCWXDq+9i/8zH6Pzadznj4x845L7dkQTPbGxgTU0na2o7e0sUNotgRr6PucV+5hb7OWVqNrML/eN2J9weT7ItFOGsrMHHPXRHEtR3RrBaBLohsVs1KnM9E6aqTDk6auTzJNERihOKJTGkJNNlJ8N96HrUhG7w6vYWHl5Vyxu7WntrT2YX+blkbiGVeR4unVfYO3dSbO9eqi9d3pvetWQJFQ8/BJjVJ4m6OvYsu2jQcwm7nTpvnIIusBrgmDWL2A6zy2zJz+/Bf9Hg6Y43q194C89tn6LnGz/ktBuuOqK0rYEYa2s7WVfbxZaGbrY29NCeGsSY7bFz5vRcbjitjNOmZqsvXWXUqQf1TBLZHjvZniOYd96icdHcQi6aW0hbMEZ3JIHLZqHQ70y7+4zt3Uv97V/s/SIHKPr+9/EuPat3WQiBfcoUZm1YT7LZnOoYzYKMx4nvrSb83mo89fVYszJJtraRqKvrTVv/hdsQ9/4a33kDq7yON8mkOSbEaj3yqqA8n4OL5xZy8VyzBCelpKknyjvV7azY1c6/tzfzzw0NzC/J4FNLp3LpvCLsamI4ZRJQgWGCyvU6yO03PUJ/kQ0b0oJC1kc+QqK+nsjmzXjPPTft7lRzOLCXpT8BzV5Rjr2ykujGjTT8l9lJLOuGG8j++Mdou/9+EvtqiW7dekIEBqGbkxJKy7H/KQghKMpwcc3iUq5ZXEo0ofPE2jp+9+ZebntsPV93bObsmbl8/IypLCgd23YJRTkSKjBMQhlXXYW9vJz6224n2dJC5yPpvUts5WXYS6dgK5tC5vuvxVZaQsfvf098Xy2JxkbiNTUYgUBaGtfixWRcfhmZ1147lh9l3GnSAMAYhUepOm0WbjitnA+fUsYbu1p5YUszL25p4tlNTWgCrlpUwhcumMHUQ0ynrSjjQQWGSUbv6aH6yqtINjUNvZMhCa1aBW+9Rdejj2EtLiLZ2IR96lRsxcX4L7+Mrr/8FXQdS1YW+V/+MhmXXzZ2H2ICsRhmYNBH8RnbmiY4d1Y+587K587lJ/Hc5ibe3t3GU+vq+dfGRp787BmcVOjrbTtSlPGmAsMkIQ2Dhv/6Gj3//GfvuikPPIA1O4vozp0Ennue4OuvA5DYvz8tbbLBnBBPD/SQ3NBKvLoaUlUoM1e+PUafYGKydJqPnJSRyJicz+e0cV3VFK6rmsKZ03P5+lObufwXK7BbNabneZlV6GNmgY9ZhV5mFvgoyXSphmtlzKnAMEl0P/10b1Aouftn+C66CJG6y3XOmUPm1VejB0PEq/eQbGsj2dZGfM8eYrt2o/l8oAksXi/C4UTv7saSl0v2jTeN50eaECzbzKeIiYb6w+w58j5YZY56f3V7CzubA+xoDrCqur33wTxgDoasKs9C0wROm4WSTBezi3zk+5zk+xy47BbsVg2bpqmBYsqIUYFhkvC87319C1L2BoX+LF4PrgULBqxXDmFxFfzzSRL5g090ONqyPXauXVKatq47kmBXc4BtTQGe29TI2toudEMSTei93WEHY9UEmibQBFg1DU2Aw2Yhz+ugJMtFrteOVdOwaAKrJphZ4KOqIouKHI8KKkoaFRgmCVtBgdlW0NBI0/fuItHQSPbHP4awqJ4tx8KSY47sNSbQdcxw2aiqyKaqIpubTk8fd94RilPTHqKlJ0ZrMEYsoRNLGiR085U0JFJCUpcYUhKJ67QGY9S0hVi/v4tkap940iCWNNtXSjJdXL24mA9VlVGWM7bP1VAmJhUYJpHpr7xCaMUK2n/7AC0/+QktP/kJ055/DntFxXhnbdLSHA4MwIgnDrvvRHCkY2OGYhiS3a1BVu5p59/bW/jVq3v41at7eOiTpzG/NIMMl5rU7kSmAsMkIoTAu3Qp3qVL2XbSbAD2XHIpzrlzcS05Gc/p78N79lKEVf23Dpdmt2MAMhYd76yMKS1VlTSzwMfHzqjgsXdr+dqTm7jxd+ZEyDMLvFw0p5Brl5Sq7rQnIDUlxiQlpSS8ciWh994jsmYt4Xf7Hrdd9vvfYUSj2EpKcVRORdiO7O6v5tlnqH3nbTjw6J6+f3pnL+3/eyMPbO9dJdPXD1jXf1n2Xxxw7LRz9qaUA7Yd2EEevK7fvgldZ2dLPXneDPK85kSBsWAYWb2bPdMWY5RXpPY8qL69X68gMcjm/isOt73vcAefY+g0FquVD97wAUqK8g/eaUR1hxNsqu9mQ10XL25tZsP+LgC+eOFMbrtwxqieWzl2aq4kZYDmH/yAjj/+acB6ze3Gd9FFFHz9Tiy+wz9MHuB3111Bl5hgvxeD/J4O+C6VQ6xPbew/iE1LjV84sHdCsyCH6BYq0oLe8IhhJBhsn6GagP0XXM8tt954ZJk4BrohmXbnswCcNyuPB28+dczOrRwdNVeSMkDBHXeQ/5WvEFyxAs1uR/N6ie+rpeErX6H773/HtXABWR/+8LCOJSUU2hxcc/e9AAi0vptmTQORmp5ZCFILqbep5QP/phKJ3h4vB/ZNpU2tFv2fQJb68hYH9jvB1Te28tjtN2Okxp0MJZrQufpXb9HcE2VxWRafP386J5dlkdANtjb0sKslyEmFPrI8drbUd9MRitMeiiMEJJKSXS0BOkJxOkJxuiMJGrv7qtZ+fcOS0f6YygSjAsNxRFit+M49lx2rmnj511uZurCMGXl5JFtbafrO/+A9+2xsJSXDOpYmNNx5o1t1oRzegW7JRm8Jx7yb/+jvV1Hf2TcoL2lI6joj2CyCjXVdvP/Xb7NwSmZvdRCYDdd+p5Wa1HTh/eV6HUzJNh8XO78kA5fdgsOqcfuFM3HZJ06PLWVsqMBwHHK4zP/WrCIPOZ/+D5q/az6/d/cFF1J0111kXvv+8cyecgQsgxSagrEkb+1uZ26xn+n53t5attOm5vDFZTPIctt5ZFUtv39rLwAXzSkwp+N4ahP2ftNuOKwaa7+xjGAsSb7PoUpoSi8VGI5DFQty+cw9Z9J69z00P/hg3waLBduU0qETKhOO0My79e6WZl5b8R4A4ZhOQbSZK8syuOn8SmwOBzaHMy3dLWdXcvOZFbQF4xT4zVl6/76+ng37u8h02/A6rPzH2ZV4HNbex8sqygHqN+I41fKjH9P5yCPYKyoo/OY3cJ9yyvB7J4mDev4o48btsqOjYdu+gjXbV/Suvw4IPgr3PgpOn5/PPTDw+c1Wi0ZhRl/A+Mt/vG/APooyGBUYjlOa1wtAvKYGzxlnHFFam2YhkZwcA76Od16Pm7O/fBdNTc1p62PNdUR2rKa9toZooIeOhjp82bnYnM4hjqQow6cCw3Eq68YbaL//fgCCb7yB9+yzh53WYbMTiA5soFTGx+mnzAfmD1gf7rmGe2+5AYAHv/hpAK76yjeYXnXaWGZPOQ6pwHCcsuXnU3TX92j8+n+z/9b/wJKZSeF3voNr0UKs+fmHbGi02mwkI6oqaaxJKWnas5NY+PBBuWbDWsLdXdgcThL9Rm27fP7RzKJyglCB4TiWee21+C6+mOYf/pDup/5O/W23AaD5/VQ8+giOadMGTacJTbUxjIOO+v088vX/PKI05QsWUzR9JuULFlM8czbaBJoMUJm8VGA4zlm8Xoq/9z2KvvUtwmvW0PzDHxHbvp3aT3ySzGuvxZKZiTUvF9eSJdjyzXELQhvOuF1lpCViMQCWfuTjlMyac8h9LVYr+ZXT0DQVCJSRpwLDCULYbHhOP52Kxx+j+fs/IPDCC7Tde2/aVBPTX3sVW2FhqsSgjLUD1XvZJVMoOenQgUFRRpMKDCcYzeGg6Dvfpug730bqOnp3N7vOOBOA7r//g5xbb0EIgQFE6rZgJHVikQiOjFw8xYNXPSkj48AoZ7n/PcjoAc0KFhu4siB/TtpkfooymlRgOIEJiwVLVhb+5cvpefZZWu++m9Z77iFZmE0iP5Nf/+d/pe3/ybu+S+b0xeOU2xNAZ635883/gw3t6dvy58CSm2HBB81AoSijSAWGE5wQgpL/+18K7ryDwEsvkWxtZUntFkqbVmDNNninbQoR3U6uO4m39KTxzu5xzYh0A6Cd8nFYuhz0OOgJ6KiGtX+E575ivq78JZysntetjB4VGBQArLm5abOvlgN07qPkh2fweO182sJWfv/JK/B5HVgGPm76EEa++kPTBEs/+hkKTls+4sceT3rcnBTPkl0Opf1mT648B6puhm3PwOM3QPvuccqhcqIY9p+4EMIihFgnhHgmtbxICPGOEGK9EGK1EGLQCduFEDVCiE0H9hupjCtjIKucgq++wadvXMwlRTsocgWwxruR0Z5+rwBScoiXTHsZx/hK6gb72iS1q14a76sz4owDgcHuGnyH6ReaPx3eMcqRcqI6khLDbcA24MAImh8D35FSPieEWJ5aPneItOdJKduOOpfK+MmZhv2yHzB36ReYu+tFaN4MPQ3QXQdNG0EakFEGF30X5l496tkJN9Vw722fx2I3J4ZLBLsgETGrXfo/Va73Xb8Sy4HG2/5drvo16Mq+B0QMPPEQ6wT9njVxYD9x0DroN6Cw33G09PuyeDBVlWQb4pnONidklsF7v4fK86FUPSdBGR3DCgxCiFLgMuAu4Eup1ZK+IJEBNIx47pSJw18ESz6Wvi7SCWv+AC9/G/76Mdh8BUw9B3xFZsBweM0gYnNBdiUULTrmnjV6LATAq69uZuUblxDVj7/aUKvjEPMdfeghePxGePASWP4TWPLxMcuXcuIY7l/V3cBXgf7PhrwdeEEI8VPMKqmhZmqTwItCCAn8Rkp5/2A7CSFuBW4FKCsrG2a2lHHlyoKzvggZU+CJT8K+t2HbP4fe3+GH8+6EedeC9+geAuQurGRGsY1E0sDv9+PPzUWzOUBohwk66c+WHrDrgdJG//VywJv094NsH/RRub3r5OCr+3F4vOQtPn/ghgOKFsKtr8MTn4J/3gbd9XD+14feX1GOwmGf+SyEuBxYLqX8rBDiXODLUsrLhRA/B16XUj4hhLgOuFVKeeEg6YullA1CiHzgJeD/SSnfONQ51TOfJykpoaceQq1miSERBX8xxIOw+UnY/gy07TT3zZkOpadA5XlQOB8cPrA6zJ8WB3TtM/vwOzPA7lV9+A9m6PCDKWag+MRz450bZQIYyWc+Dycw/AC4CUgCTszqoyeBK4BMKaUUZgVqt5TykDN4CSG+DQSllD891H4qMBynpISWbbDzeahbDbteACM5/PQ5M8weOq5s2PoPszH24rtGP2iEO2DXS1D3bqoLadLM94HBZ+5sM9AVLTLbAMYqiD33NXj3frj1VTNAKCe0MQ0MB534XPpKDNuAz0gpXxNCXAD8WEq55KD9PYAmpQyk3r8E/I+U8vlDnUcFhhNEpBNatkP3fkhGIRmDWMD88vUWmNVDnXthxc/60jgzINo98FiX/8xsEJ//QbM9w+o4trzVrDCDWKAJ3kzdxzj8YPeYI5I1q5nPcAckI+lpP/B7s7pstIXa4d4zzPafW18b/fMpE9pIBoZjabm7BbhHCGEFoqTaB4QQxcADUsrlQAHwVKpHhhV45HBBQTmBuLKg/H3AYZ4sduG305cNHbb/yyxxrHvIXPfMF82fb/6v+fMb7WAZ4te7cx+sf8QMSHaP2UD+vs8Bwrzzljr84bL0NLOvhA/+cUBPIgDiIWjdDm/8FHY8C3/7BMy+aujzjxRPDpQsMc+tKCPoiEoMY0WVGJQjIqU5Ovj5r8GuF811ebNh4YfML+3q16Bh3cBqK806eFXWohtg/cPp677ZOTAoBJpg5a/MEkw8BOF2qH7V3HbTUzDtEI3II0FK+OkMs0rtmvtG91zKhDdRSgyKMjEIATnT4Ia/Qjxsfqmv+o3ZjVZYoGAuLLge1qdKF5nlZoP3B35vVlf9+3vw1t19x1v/MDgzIdrVty7aZbYlHBDpgv+d1becXWk2nFd90gwIleeN1qft07TJbOivOGv0z6WcUFRgUI4vdjeceov5CrWnejqlRgpf/avB0yz7jvkydLONQ7OaaVp3mqWA4sXm4LL+9Hj68jX3w5RTRv7zHMqOVG+k6cvG9rzKce+IZr1RlEnFk3Nk00doFnBl9qXJm2m2gRwcFMAch3HRXX3Lax40q5PGip4wBxdOPRt8BWN3XuWEoEoMinK0zvg8BJvh7Z+b1U9bnjIb1C026KyBuddA3RqzuicZgXPvNLcv/JDZu+pY7HgOAg1wzldH5KMoSn+q8VlRjlUiYnZv3fk8xIJQ9x507Dl8OlcW/Mcb5tiHI7X7ZXjoWtBsUPUJuPBbZg8r5YQ1buMYxooKDMqkFwuYbRY2t9nA3brNnDJky9/NQXqv/aBv3zNvN7udVpxljhh3ZQ/eLfZgHdXmGI+1f4K8k2Dh9XD658A6xCR8ynFNBQZFmeyaNsMflg8+WA8gq8IcrDdrudn43X80ddMmc/xGqA3cObDnlb5nNNzwBMwYMDONcgJQ3VUVZbIrnAdfqzXHImz6mzlYr2CuOU9UpMOsmnrjJ+YrdyaccgvM/4BZfdSw1jxGVgWEOyGWCi6aFbKnjttHUo4fqsSgKBORlGbJoGEtrP0z1B/09zDvWnMcBkAybgYTq0M9D/oEpkoMinK8EwKKFpivkz9mNjZXvwbNW8CTCxf3a6Ow2sFXOG5ZVY4/KjAoykQnBMxYZr4UZQyoAW6KoihKGhUYFEVRlDQqMCiKoihpVGBQFEVR0qjAoCiKoqRRgUFRFEVJowKDoiiKkkYFBkVRFCXNhJwSQwjRCuxLLeYCbeOYnaOl8j22VL7Hlsr32BpOvsullHkjcbIJGRj6E0KsHqn5P8aSyvfYUvkeWyrfY2us862qkhRFUZQ0KjAoiqIoaSZDYLh/vDNwlFS+x5bK99hS+R5bY5rvCd/GoCiKooytyVBiUBRFUcaQCgyKoihKmjENDEKIDwohtgghDCFEVb/1y4QQa4QQm1I/z++37XkhxIZUuvuEEJZBjnuo9K8JIXYIIdanXvmTJN9LUut3CyF+LkT/p8GPa55zhBCvCiGCQohfHrRtIl/rQ+X7mK71aOY7td8dqbztEEJc3G/9hL3eh8n3mF9vIYRbCPEvIcT2VLofDnFcuxDiwVT6DUKIc/ttG/PrPUL5PvLrLaUcsxcwG5gFvAZU9Vu/GChOvZ8H1Pfb5k/9FMATwPWDHPdQ6dPONYny/S7wvlT654BLJ0iePcBZwKeBXx60bSJf60Pl+5iu9Sjnew6wAXAAU4E9gGUSXO9D5XvMrzfgBs5LvbcDbw52XuBzwIOp9/nAGkAbr+s9Qvk+4us9po/2lFJuAzg4YEkp1/Vb3AI4hRAOKWVMStmTWm/FvDADWssPlX4y5hvIxvzjW5k675+AqzH/U8c7zyFghRBi+nDzciTGOt9CiCKO8VqPZr6Bq4DHUr/Le4UQu4FTgZVHkr+Jkm8hRA3jc73DwKupfeJCiLVA6SCHngO8ktqvRQjRBVRhfrkes7HOtxBiP0dxvSdiG8O1wLr+X+pCiBeAFiAA/O1I0wMPpop+3ziaYuswjWS+S4C6ftvqUutG2rHmeTCT4Vr3N1bXGo4u3yXA/kPkb6Je76HyPa7XG0AIkQlcQeqL9CAbgKuEEFYhxFRgCTCl3/Zxud7HkO+jut4jXmIQQrwMFA6y6etSyn8cJu1c4EfARf3XSykvFkI4gYeB84GXjiD9DVLKeiGED7PYexPwpwme78F+4QbclY1nnocw4a/1YIccZN2gfbjHKd+Hyt9Evt5D5Xtcr7cQwgo8CvxcSlk9SNLfY1b3rMacr+1tIJnaNm7X+xjyPezr3d+IBwYp5YVHk04IUQo8BXxUSrlnkONGhRBPYxZRB/zRD5VeSlmf+hkQQjyCWQwf8J85wfJdR3pxsRRomCh5HspEv9ZDGNa1Th1/PPJdR/oda2/+Jvj1Hirf43297wd2SSnvHuKcSeCL/Y71NrArtW08r/fR5ruTYV7v/iZEVVKqiPQv4A4p5Vv91ntTdcAHIuZyYPsRpLcKIXJT723A5cDmiZ5vKWUjEBBCnJ4qrn4UOOSdxljl+RDHndDXeiijea1HKN9PA9cLIRypKoIZwLuT4HoPmu/xut6pbd8DMoDbD5HeLYTwpN4vA5JSyq3jdb2PNd9Hfb3lMbSwH+kLuAbzjiEGNAMvpNb/NxAC1vd75QMFwHvARswGmV8A1lSaK4H/OUx6D2br/IH095DqGTGR853aVoX5i7cH+CWYo9THO8+p5RqgAwimzjFnol/rofI9Etd6DPL99VTedpDqUTJJrveAfI/j9S7FrELZ1m/9pwb5m6xI5Xcb8DLmVNbjeb2PKd9He73VlBiKoihKmglRlaQoiqJMHCowKIqiKGlUYFAURVHSqMCgKIqipFGBQVEURUmjAoOiKIqSRgUGRVEUJc3/B182703RB16yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1 and vtdGeom[p].centroid.x > -123.4:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0218d8e6-7832-479f-b44f-b37c117ee40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5d698e51-7b2d-40b8-b932-d22f20f09bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c17c1582-29d1-41bc-8f56-b32ac70747ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  252909.0 people (VAP of 205434.0 ) and  147664.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "55419803-eaaf-4181-8848-d8fdad0205b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for southern central tip\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-76.2,37.8)\n",
    "pt2 = Point(-76.4,38.6)\n",
    "pt3 = Point(-77.4,38.62)\n",
    "pt4 = Point(-78, 37.8)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2a56b3e5-803d-425d-8bb6-abfba9aa9716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  142 tracts w pop 263227.0  to reassign to tracts...\n",
      "[19, 20, 22, 23, 29, 30, 344, 345, 458, 648, 651, 652, 688, 689, 690, 709, 710, 712, 713, 715, 717, 718, 732, 764, 767, 885, 887, 941, 942, 952, 964, 965, 986, 990, 992, 993, 994, 996, 1000, 1446, 1447, 1448, 1495, 1496, 2093, 2156, 2777, 2779, 2872, 2873, 2874, 2895, 3144, 3246, 3287, 3288, 3438, 3441, 4029]\n",
      "I have flagged  75 precincts to reassign to precincts...\n",
      "[297, 300, 340, 347, 348, 349, 368, 433, 436, 481, 526, 546, 757, 759, 760, 762, 773, 775, 776, 777, 778, 783, 784, 786, 788, 1669, 1671, 1672, 1676, 1677, 1678, 1680, 1689, 1700]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 and tractGeom[t].centroid.x < -76.5:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 and vtdGeom[p].centroid.x < -76.5:\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "264c0a29-0366-433f-ae45-63b986566b34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "34f94200-4cc7-4226-9a6c-d6ae75b3380f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAD4CAYAAAD7CAEUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAC17klEQVR4nOydd3hUVd6A3zO9JJn0npAQQgm9I00BUYq917Wsva2u7qduteza1rKua1l3XXvBLqAoiID03lsgJBAS0nsm08/3x51AQtokmRRw3ufJk8y555x7bmbm/u75VSGlJECAAAECBPAVVU8vIECAAAECnFoEBEeAAAECBGgXAcERIECAAAHaRUBwBAgQIECAdhEQHAECBAgQoF1oenoB/iAyMlKmpKT09DICBAgQ4JRi8+bNJVLKqPaOOy0ER0pKCps2berpZQQIECDAKYUQ4nBHxgVUVQECBAgQoF0EBEeAAAECBGgXAcERIECAAAHaRUBwBAgQIECAdhEQHAECBAgQoF0EBEeAAAECBGgXbQoOIYRBCLFBCLFdCLFbCPG4t32EEGKdEGKbEGKTEGJcM2MHeI/X/1QJIe73HpvXoD1HCLHN254ihKhrcOwN/15ygAABAgToDL7EcdiB6VLKGiGEFlglhFgEPAE8LqVcJISYAzwHnNVwoJRyPzACQAihBvKAr7zHrqzvJ4R4AahsMDRLSjmig9cUIECANthdspv1BevJiMhgQtyEnl5OgFOMNgWHVAp21Hhfar0/0vsT4m23APltTDUDRSA0CjgRQgjgCmC678sOECBAR8ityuWlLS+x5PCS423Pn/k856ac24OrCnCq4ZONQwih9qqSioAlUsr1wP3A34UQucDzwKNtTHMV8HEz7VOAQinlgQZtqUKIrUKIFUKIKS2s6TavimxTcXGxL5cRIMAvmo/2fsQF31zAqrxV3DXiLpZdsYxYcyw/5PzQ00sLcIrhk+CQUrq9qqNEYJwQYghwJ/CAlDIJeAB4q6XxQggdcAHwWTOHr6axQDkGJEspRwK/BT4SQoScPEhK+aaUcoyUckxUVLtTrQQI8IviqwNf8fSGp5kcP5lvL/6WO4ffSaQxkozwDA6UH2h7ggABGtAuryopZQWwHJgF3AB86T30GdDEON6A2cAWKWVhw0YhhAa4BJjX4Bx2KWWp9+/NQBbQvz3rDBAgwAmWHl7KY2sfY2L8RF4860WiTCcetPqF9SO3Ohe7296DKwxwquGLV1WUECLU+7cROBvYh2LTONPbbTrQ2mPLybuKes4G9kkpj550PrX3775AOnCozSsJECBAI1YeXcntS27nwRUPMiRyCC+d9RJatbZRn/TQdNzSTU5lTs8sMsApiS9eVXHAu96buQr4VEq5UAhRAbzs3TXYgNsAhBDxwH+llHO8r03ATOD2ZuZuzu4xFXhCCOEC3MAdUsqydl9ZgAC/YKxOKw+ueBCL3sIVA67gnpH3YNKamvRLCk4CILsymwHhA7p7mQFOUXzxqtoBjGymfRUwupn2fGBOg9dWIKKFuW9spu0L4Iu21hUgQICW2V++nzpXHc9NfY6zks5qsd/Okp0ApIeld9PKApwOBCLHAwQ4DdlTugeAQeGDWu2XU5VDkDaItNC07liW35BS8n1xJeVOV08v5RfJaVHIKUCAAI3ZXbKbSGMk0aboVvvVOGoI0gV106o6h8sj2VFjRSCYvTnzePuT/RK4Lj4Co7rxc7CUEgmohOjmlZ7+BHYcAQKcZtjddlYcXcG42HGINm6aJbYSwvRh3bSyjuORknM27WfO5gONhAbAnw7mkfrzDn69K5tCu/N4+2/35zJ+3V4cHk93L/e0J7DjCBDgNGNjwUaqHFXM7Tu3zb551XmnhH3jzwfz2FNrAyBBr+XB1Fiujg3ny8Jy7t57BIBviyv5triSSK2GCpcLl1TG/mpHNv8ZkkKwRt1Tyz/tCAiOAAFOM4J1wQC4PK3r/z3SQ35NPtOSpnXHsjpMjcvNZwXlAOSdNRx1g13UpbHhpJr01Lk9XLotC4CSk+wey8ur+ehYKbcnta62C+A7AcERIMBpRnqosoM4UH6A6cktp4Arthbj8DhICErorqW1jy3v4dj4P2YPeo5Kt473hqY2Ehr1jAoxK93PyCBUq8GkVh23b2yvrmP25kwcHtnNiz+9Cdg4AgQ4zTBpTcSYYjhSfaTVfvXHk0KSumNZ7SNzMcy/F92xrVyU9QEAU8OCWx0Sb9Bh8hrIhRCohGBEsBGAuoCdw68EBEeAAKcZVqeVakc1Ro2x1X7VjmoALDpLdyzLd9wu+O6h4y8Lo4azf/IQDOr2366EEJjUKt7NK8XmDggPfxEQHAECnGb8b9f/sLqsnJ92fqv9YkwxABRaC1vt1+0U7YGKw1QMugSAy6OCsWg7rlW/PCaMUqeLDZW1/lrhL56A4AgQ4DSiylHFh3s/ZGafmQyPGt5q31hzLADHao91x9JaR0p47Qx4zALLnwFAZ1MM4m5H5274j/SNA2BHtbVzawxwnIDgCBDgNOKjvR9R46zh9mHNpYZrTLghHL1az9Hqo2327XKK9ys7DYD93wLgHHw5AG5H52742XV2DCrBPq87b4DOE/CqChDgNKHOVceHez9kauJUnxIWCiHIiMhgR/GOblhdG9i8laOv/QJih0BFLqrQVFgIspM7jj8fyEMtBFPaMK4H8J3AjiNAgNOE1XmrqbBXcN2g63weMyp6FHtK91Bpr+zClbWBxwPrXlP+NoRAcCwkjQWNHgC1q3O1QjRCMCzYyJVx4Z1daQAvAcERoMvYsSyXL57bTEVhQLfc1Ugp+WTfJ4TpwxgbO9bncbNTZ+OWbh5Z+Qjz9s1j6eGl1LnqunClzZC/FfZ8rfwdnXG8uaq2AgCNoXM7hXWVtZQ73Z2aI0BjAoIjQJdQXlDLynkHKDhUyYJXtlFV2s03o18YS48sZX3Beu4acRcale8a6AHhA7g+43pW563mr+v/yv3L7+eKBVdQUlfi/0Vay2DbR4ohvFF7qfL72s9BfyLhorWqCAC1udmqDD5R61YERq7N0eE5AjQlIDgC+J2qkjo+emw9AHPvGoat1sX8l7fhsAVSYHcVH+79kD4hfbi8/+XtHvu7sb9j8/Wb+enyn/jntH+SX5PP42sf51jNMUrrSpv0P1h+kCsWXMFf1vylfbuThQ/A13fC4TUn2pw22P6R8ndE49TudWVKgKIutPMBir9Nie30HAFOEBAcAfzOqs+UKsIjZiaTMiySuXcNpaq4jiX/24PTHlAZ+JsqRxVbi7Yys89M1KqOJfLTqrREmaKYljyNe0bew/Lc5ZzzxTmc9elZ/Oan31DlqDre99PMT9lbtpevDnzF9d9dz2eZn3GkqpUo9dIs+PbBE+qoI2tPHPvhUdj9FQg1mBvnkrKW5QAQFdO3Q9cEoEJJUeI5eZcToFMEBEcAv1JZbCV7Rwmjzk1m0qX9AIhPD2PyFenk7Czhs2c2UVEUsHn4k3X563BLN1MTp/plvhsH38irM17lz2f8mduH3c7PeT9z79J7sbsVI3VhbSH9Qvvx6oxXKbWV8sTaJ7hy4ZXN7k4AyPoJNv73xOuNb0FFLnz/KGz6n9L2q28aqakAPCWZWFUGosI6nktL7U1tFUhV5V8CgiOAX5n3t40gYdi0xuqFYdOSuOC+EdRVOVjwynbqqgM6Z3+RVZGFQDA4YrBf5hNCMDVxKpf3v5x7Rt7D05OfZkvRFm5bfBs5lTkU1xUTbYpmSuIUfrzsRx4a8xA1zhpyq3Obn9BRo/ye8iAMOh+q8+EfQ054Uo29FVKnNBkWeWwjhyKGoergLgoa7DgISA5/EhAcAfyGrcaJ06aoosyh+ibHkwaFM/fuYdRW2Pnu9Z24Ap4ufqHaWY1BY0Cr0nbJ/LNSZ/HslGc5UH6AS+Zfws6SnUQaIwFQq9QkBSsPCTq1rvkJaktAa4IZf4a0GU2Pj7utSVNOSR79qg9iT57UqbWrAjuOLiEgOAL4jZI85cly6FmJLfaJ7Wvh7BszKDhUydJ39yID3+hOkx6aTp2rjszyzLY7d5A5fecw/+L5nNf3PLQqbaPdjcOt7B5VooXbiaMWVFrwuGHMTXDnWrjiPUgYDXOeh6j+TYbk7PwOFZL4wbM7te538hTvsED1WP/SpuAQQhiEEBuEENuFELuFEI9720cIIdYJIbYJITYJIcY1M3aA93j9T5UQ4n7vsceEEHkNjs1pMO5RIcRBIcR+IcS5frzeAF3I5kU56AxqRs/q02q/fqOjOePiNA5uKuLfv1nRoXNJKRn62A/85pOtHRp/OpFXkwfQZjbczhJpjOSJSU+w5fotXDPomuPt646tI1gbTKoltfmBqVPAXqnYOgBiMiDjQrj1Jxh3a7NDNIeWUq4LIy7F95iU5jjsdcO9KjYQ/OdPfNlx2IHpUsrhwAhglhBiAvAc8LiUcgTwZ+/rRkgp90spR3j7jAaswFcNurxUf1xK+R2AECIDuAoYDMwCXhNCBGo+9nIcNhdH95WTMiyyWTXVyfQdGQWA29mxVNeL9xRSbXPxzbb8Do0/Xah11vLxvo+ZnjSd5JDkHlmD3W3H4XGgU+mQzXkvDTxP2XEc/NGn+epcLgYVrOFw/GRQdU4pEuHNqmsL7Gz9SpvvilTwWrfQen+k9yfE224B2voGzwCypJSH2+h3IfCJlNIupcwGDgJNdjMBehdL/qckqIvt61tth+1Lc1FpBDc+2zEd9tPf7QXgj3MHdWh8b6HKUYXN1fHke29sf4MqRxW3DL3Fj6tqH1GmKOxuO8PeG8Zbu95q2qEqHzxOiGo7fxbAngNriHBWou0/s9NrSzUqDzHv5XdBQOMvGJ/EuRBCLYTYBhQBS6SU64H7gb8LIXKB54FH25jmKuDjk9ruEULsEEL8TwgR5m1LABq6Zxz1tp28ptu8KrJNxcXFvlxGgC6kKKcKvUnD4Cnxbfatq3Gwb80xBoyPxWxpe3dyMnuPVZFTqrj0Joeb2j2+t3C0+igXfHUB1y+6Ho9s/85r6eGlvLP7HS5Nv5ShUUO7YIW+cf2g67m438UAOD3Oph2yverI+FE+zVe7fwkeBCmDZ3V6bXOjlAcZTcDI4Vd8EhxSSrdX3ZQIjBNCDAHuBB6QUiYBDwDNPGooCCF0wAXAZw2aXwfSUNRfx4AX6rs3t4Rm1vSmlHKMlHJMVFSUL5cRoItw2t1YqxwMn5GEyocqbbtW5OFyehgxo2OqlQ/Xn9i0JoR1rV6/q/g+53uu/e5aSm2l7Cvbx7aibe2e45P9n5AQlMAfJ/zR/wtsB1GmKB6f+DhB2iB2Fu9s2mHPfAiKgZghPs0XXLCVnKC+mC0xnV6bSgiMKhXWQPU/v9IuBaKUsgJYjmJ7uAH40nvoM1pXJ80Gtkgpj5cak1IWegWSB/hPg/FHgYZBAIm0rQYL0INkbVFyCkUkBLXRE1xONzuXH6XPkAjC483tPpfT7WF+A7tGYtipt+M4UH6Ah39+mHhzPP89RwmM21K0pV1z5Fbnsv7Yes5PO79duam6CiEEWpWWMEMYLo8Ll8fFtqJtvLf7PQ66a5Buh6Ku8gGjrZxqr7uvPxhrMbGirLp5+0uADtHmJ04IEQU4pZQVQggjcDbwLMrN/EwUQTIdONDKNFdzkppKCBEnpawvPXYxsMv793zgIyHEi0A8kA5s8PWCAnQv1ioHK+dlEt0nmOSMtj1XCg5VUVftJGNy2yqt5tiQXUaVzUVsiAGrw4XF2DWxC12FR3p4dsOzBGmDeP3s1wk1hJIRkcHnmZ+TFJzEyOiRRJuiW53D7rbz8M8PY9AYuDT90m5aeduU28uZnzWf+VnzG7WPtdn4X125Ej0+8d425xFqLS6P/2J8ZkeF8mjmUTKtdgaYDX6b95eMLzuOOGCZEGIHsBHFxrEQuBV4QQixHXgKuA1ACBEvhPiufrAQwgTM5MTupJ7nhBA7vfNOQ1F3IaXcDXwK7AG+B+6WUgYixXohbpeHb1/djtPhYcaNGWh0bTu/mS1KkJizgwkPl+0rQqdRkRJp6vrdhscDC38L+771y3R7S/fyq0W/Yn3Ben4z6jeEGkIBeHD0gxRZi3hoxUOtp+5AcUN+bM1j7CzZydNTnj5e/rU3kBKScvzvSQmTeGjMQ/x0+U9sNBrI1Grh0HLfJtIHo7dX+m2HcG6E4sOztLSqjZ4BfKXNHYeUcgcwspn2VSgutie35wNzGry2Ak3yIkspr2/lnH8D/tbW2gL0LNuX5lJ0uJrxF/YlPM43tVNIpBEhoKKoY2nWV2eVMjo5jDqnm4igFiKV/cXeb2DTW8pPn8mQOBoSxkDiGAhp347px8M/8uCKBwnVh/LkpCe5MO3C48fGxY3jp8t/YlvxNh5c/iCXL7ic8/qex+X9LycppHHqlnd2v8PCQwu5Z8Q9zEhuJgq7B3l43MPc+eOdAPxr+r8aqdCsKqEEAPqAMySJhKKtVLrchGo7r4arj+WI0fW8Su90IRA5HqBDeNweNn2XQ0xqCGNmp/g8Tq1RERRuoLK4/YJjT34Ve49VcdaAKEJNWsqtXZjvymGFJX8GSzKccQ+46mDta/Dp9fDiIHjMovwc3ezTdB/v+5h4czwLLl7ARf0uQpzk5RNqCOWspLN4Y+YbDAwfyPt73ueCry9gU8EmQNlpzM+az0ubX+KcPudw27CmaTp6mvGx44//XetUyr3uK9tHmiUNIdTgo+eYCE8hzFVNfkWRX9b1RUE5JrWKWVG+uYoHaJuACA7QIYpza3Da3T65356MJcrYIcHxr2UHCNZruGpcMkfKrGzPrWj3HD6z6kWoOAI3fgspk5U2lx0KdsLRTbDkT+B2wP7vlJ1IG2SWZzIjeQYhupBW+42NHcvY2LEU1BZww6Ib+NPqPzE7dTZLDi8hpyqHEVEj+OvkvzYRPL0Bh+eEILfoLeRW5XL5AqU+iF6jpxnnyGYxRihp1CuKD0FUx2xh9dg9HhYUVzAtPBizOhBH7C8CguMXRMHBTNZ//SmOujpMllASBg5mxDlz2h54Em6Xh/XzDwG+B/w1JDIpmB1Lc7FWOTCF+KZuyiys5rudBdw7vR8Wo5b4UCPlVidWhwuTv1UQxfth9csw7MoTQgOUGtiJXlXV2F/Dk5HH62K3hUVvOf4U7gux5lienPQkd/x4B2/teosxMWP49dBfMzd1Llp173QIMGvN3Dr0VkZGK5pt2UBQuN1OaCmX1UmERiuCw1p6CJjceuc2qHC6qXS5+TFg3/ArAcHxC6G8IJ95TzyKzmDEEhNLzo6tZK5b3W7BUV5Qy9cvbcVa6aDvyChCY9pvoE4ZEsG2JUcoPVqDyQdPLID31uZg0Kq4eZKSDynRG7+RX1FHv+jO1aRuhJSw6GElm+s5f225n1qrFB/yMeo72hTNvrJ9uD1un4stjYsbx9LLlwIQZghro3fv4L5R9+F0O3F6nESZTsRXmYPjoa7CpznCIpX32F3eSnEoHwnTKv/rEE1gt+FPAjaOXwhbvvsG6fFw3dP/4Jonn2f07AvwuF24Xb751teze1U+1koHEQlBTLtuYIdUJkbvLqOu1jcbhcvtYdHOAmYMiiHMrIxNCFUEx9FyP9cyL8mEQ8tgym8hqHW3WITwWW9/Wfpl5FTlsOJo+5I6hhnCThmhAUrk+KgPRjHq/VEIBCuuVK7XZY6C6gKf5hDGMKo1QWgrW6jv0Q7qvIF/k0LbjjEK4DsBwfELwO1ysm/1z/QbM4HgCCWwSmtQ/Nmddnu75lJ5Cxxc+MAIDOaOqUz0JmWja6/1zSV3TVYppbUOzhsad7wtPToYrVqw6oCfcxAdWKL8HnRB230tiVDSWvjSCWamzCTcEM6Sw0s6sbjej9114vP05LonqbIrKqIsdy3UFoHbt/e81BSPyZv11x+MDDn1AkV7MwHB8Qvg0JaN2GqqGXzmCfdNrb5ecLQvwV5pXg3h8WaMnXCFrRc4dqtvu51PN+ViMWqZNvDEDsBi0jJjYAxfb8vzb0Twrs8hZiiEt5AivCEpkyFnlU9uplqVliGRQ9hdutsPi+y9HKk+oV6anzWfbcXbANhkzVN2Z7W+5ZWrCY4nvLbzCSN6oxPB6UBAcPwCyNq8AYM5iD7DToTjaPSKUddp823HIaXk54/3c2R3mU+pRVpDrVGhNaix1bT99Fle62Dx7kIuHpmAQdtYTz0syUJJjQO7y095iKryIX8bDJzrW//UM8FWoXha+cDomNFkV2ZTUnf6ZmqttFc2el1fB72o/k5T45u6yhGSRFzdMWwu/8T+BrKN+JeA4PgFkLt7J4kZQ1A1cEfU1gsOH3ccRYer2blCUR2MODupjd5tozdpqK1qW2h9vS0Ph9vDFWOantPjrbGg8tdT5Zb3AAnDr/Stf+pUQMDmt33qPj5OiXM4ndVVqZZUEoJOJLO+6OuLANDW37nVvnmhidA+BLutFFR2LvN1/ScjIDf8S0BwnOZUlRRRVVxI0uBhjdrrVVUuH20c239UVBAXPziK6D6txyL4QuKAMA7vLMXRSuoRKSXzNuYyNMFCRnzTc9bY3ejUKnSaTn6MC3fDd7+DVS9B/9kQ3te3ccGxcMbdsPkd2PNNm90zwjMYFT2KN7a/QY2jps3+pyKx5ljuH33/8dfDo4crv+0OpFoHYa1Xh6zHEK70KyvO7tR6AoqqriEgOE5zinKUL15sWnqj9vbaOGxWF5FJQcSnh/plXRmT4nHa3Rzc1HJ08K68KvYVVHPF2OZ3ODV2J2Z9J90sPW6Ydx1seBOiB8F5L7Vv/Iy/KLWzv7kHyg612lUIwe/G/o4yWxmvbnu1E4vuvazJW8PDPz9MYlAiy69YzivTX2HL1eu42qlBpJ4JOt9S01i8LrnWspxOrSfPrtjRypwdy40WoHkCguM0p+SwIjgikxo/6Z3wqmpbcNSU2zm6r5yS3JpWdwjtITbNQkRiEJsW5eByNK/HnrfpCHqNiguGNx89bHW42xf8V1sCCx84kS6kLFtJYFh2CC57G25bDiFxbU7TCI0OLn8HPC5FeLTBkMghXDXgKj7Y+8HxdCKnAw63g5+O/MRDPz9EjCmGeefPI8KopKjTbv0QTV05jL7R5/mCIlIAUFW0VTC0dXReNWaCoYvzmv3CCAiO05ySo0cIiYpBZ2zsjqjR1ds42lZVrfw0E+mRmC061J1VC3kRQjDp0n5Ul9o4tK2pHtvmdPPNtnzmDI1rMXW63eVBr23Hej6/CTb978Trf46AT38FocmQcWGLw9okJAEMoUpKEh94cMyDhBvCeWbDM6eNymrqvKn8ZtlvqHZU8x9tCiHvXKDk+3LWwYpnFHuQr04HQHCQInSkrbKNnq0T5X2wqPaTkT2AQkBwnOZUlxRjiW5aSU1rqPeqanvHoVYrT21X/GGc3wQHQMKAMHQGNfkHm94cvtqaR7XN1axRvB6704Pe14hgj0dxne13Njy4X/GIAkDClIfAx2juZtn9FVTnw6T7fOpu0Bj42+S/cbDiII+tfey0KDDUMJ1Kn93fQf5W+Hs/2PoBWEth6u+UgEkfsUvwINB20kgRpFETpdOQXde+eKUArRMQHKc5tRVlmEObRh4fN4472v5CxaYp+aj2rT3WRs/2oVIJYtMsHDtY0ah977Eqnly4h9F9whif2nJKEofbg74tQXZ0Myx6BJ4IU+IIBp2vGLVvmA+/2QHXfg6jb+j4RXg8sPJFiBwAA8/3edjkhMncNeIufsj5gec3Pd+hmuO9ibl9ld3EjOQZMOZGpdFZC989pDgbpExp13xVTicqJDo/JCascLopcQRsHP4kIDhOc+x1dehNTQ2Sx43jLew4jtocLCutQkrJgAmK3v/gZv+kuW5IXL9QyvJrqS5T1lFe6+C29zcRbNDw+rWjjkeqN4fd6W4iOKxWK7b6azqwBP47Hda/rrzWGGDYVSc6h/WB9Jmdu4D8rVC0G8bfDqr2fZ1uGXoL1wy8hvf2vMcfV/0Rp4+lVXsjGeEZAMQHxSs5vuIUbypih8K5T7VrtwFQXVsBgMrQuVTodW4PTimJ1vXOxJCnKoEkh6c5zjorOqOxSbtao0Gl1rRoHB+zdg8AuWcOP15TfOQ5yX5fX/9xMWxckM32H3OZfEU6f1+8n8JKO/Nun0B0SOtlPu0uDyFe+0dNTQ2vvPIKdq/NJiZIw7Can5gEMPdFiBoIKZP8vn7CU0Frhtz1SsbcdqASKh4Z9wjhhnD+te1flNnKuGbQNWREZBDpx5rb3cH1GdfzwuYXcHm8T/axQ6HyKNy+st1CA8BaWw6Axhjql/X1MQaM4/4kIDhOY9wuJ26XC52hqeAAJQiwLeP4lqpaag9VYgjS0m90G0n/OkBIhJG00dHsXpPPqAtSWLAtn/OGxTEyue3EfnaXB71asGLFCpYtW9boWGGNiz2kM2nmRe2+obcLUzhkXAAHl3ZouBCC24ffTrgxnKfWPcXq/NUADAgbwMSEidw4+EbCDb5lEO5JhBAMiRxCZnmm0hA3QrFvVB9rd7VEgDqrIjh0ps4leKzfsJ76VqTeRUBwnMY46pTMsc3tOKBlwXHUdiJr7Y+lVUxWKyqYrsr7o04y4dro5rKnVlDtcHHp6ESfxtldbgzCybJly7BYLEyfPp1hw4YhhODj539HRZ3ZZ4N1p4hMh+0fg6PW5ziFk7m8/+VMip9Efk0+24q3sTZ/Le/vfp/Veav537n/w6Lv/dXroo3RZFVmKS/qgygPr4Ghl7V7LodXcOhNoZ1a0/HI8YDk8CsBwXEaUy84tC0JDoOhWVXVf44WoxYQp9eysryGs/RqnPauc2cs86ZXL6pzEBOm54y+TUrUN+IfH8yn4uAWwlyxBGvNCCG48soriY8/8WSrVQucsptMeKHeGJnywxCT0eFp4oPiiQ+KZ0zsGG4Zegtr8tdwz9J7uO+n+3jznDfR+5iuo6foG9qXZbnLqHPVYUwapzRWHu3QXA5v7Q5TZ3ccXtHhCew5/ErAOH4a47B5dxyG5lNKa/RNBUeVy82H+aVcEBXK7EgLB6w2dCY1bqcHl7NrhEeF1zDeNymE/zt3YKsGcZfLTcXBLQAM1RSgK8uiX79+jYQGgEYtcHWX4AjzZtJtI3K8vUyMn8jfJv+NLUVbuH/Z/fx89Gfs7t7rVpoQlIBbupUkjoeWK41hKR2ay12nuGibzZ1T04mAqqpLaPObJYQwCCE2CCG2CyF2CyEe97aPEEKsE0JsE0JsEkKMa2bsAO/x+p8qIcT93mN/F0LsE0LsEEJ8JYQI9banCCHqGox5w7+X/MuhbVWVAddJguPD/FJq3B7uSI6mj1FPrduDzaD2ztc1gqOmwo4LyUd3T2xTTfXFT+sAKFWHowtTvL0iI5sakrVqdfftOGIyQKWFoxv8PvXs1Nk8MPoBVuWt4u6ld3PHkjt6bdyHw63sHPeX7YfM70Gta1fQX0M83sA/g6lzKrr6T0B9QacA/sEXVZUdmC6lrBFCaIFVQohFwBPA41LKRUKIOcBzwFkNB0op9wMjAIQQaiAP+Mp7eAnwqJTSJYR4FngUeNh7LEtKOaIzF3Y6kr1tM1HJKQSFN1XlVJUUk7d3F+njJ6HRKR4kTu+Ooz69yMlo9XocVuvx11JK3skrYYLFzPBgE4XePD8/rsolGlCpu8bGUVVoxagVaNRt3+h3rF+FFpgzczrnjssgKyuL1NSmtTO0Wi0uXEqcRTvdZNuN1ggJo+Dw2i6Z/qbBNxFljGJ78Xbm7Z/HyryVjIsdh06tQ+VjHe/uoN6IX1pXCqYIJW5G1TFtuKiPGNd3LqGmEIJxFjPfFlfy+75xgfocfqLNd1Uqjzf1eRG03h/p/al/Vy1AW1VXZqAIhMPeeRc3OLYOaL8F7RdE8ZEcvnz6LwCkj5vI8JlzSB4yDKFSsX/tShb+41kAjO/+B53JhKOuDmOw8vZ4XM0HP13m/Kfybtb9CYyhZNc5OGxzcE8fxXsqyZvfp1glueiSfh2u+NcapeU2LFVu6OubUVnlsoGAc8dloFKpSE9Pb7afxhSCk2o8NUWoQmL9ueTmST4D1v5LSbOh82+1OSEE56edz6SESczbP49HVz6KzWWjb2hfzkw8k8v6X0asuRuusQ00XiFRbi9XKiOG9umQKy6AsFdhVZswqTtvhr0+PoJ79x5hdUUNk8P8WJ/+F4xP74p3t7AZ6Ae8KqVc71U5/SCEeB5lRzixjWmuAj5u4djNwLwGr1OFEFuBKuCPUsqVzazpNuA2gORk/8cX9DbqqpQnsJi+6eTu3cWBDWswWUKxVlY06hc/YBBavQHp8bB/rfJvU7WVTmPP1zD6RvbUKDuUoUHKjc/mrXcRZtF3SQwHwJJlOWgRDBrftsvmtoNHUSFRx6ajamMXYbZEANVYC7II6g7B0WcirP4HHFgMgy/qklMEa4NJCk6iyFrEwIiB7Cjewb6yfSQGJ3JRv645Z3sI0ioFviqsxZD9Mwy9vMNzaexV1GqD8IcIPi8qlD8fyOO9/NKA4PATPgkOKaUbGOG1Q3wlhBiCctN+QEr5hRDiCuAt4OzmxgshdMAFKOqok4/9AXABH3qbjgHJUspSIcRo4GshxGApZdVJa3oTeBNgzJgxvVPp61eUJ7ep195EfP+BZK5bxaJXX2zSa/CZM0gfp8jwc+/8DVXFxYTHJzTpB5BvHE583XbY+TmMvpG9tXWogP5mRbVV6k1FravquojmnG0laFWSKRObX2M9Ukr+/eEXxAi45Jypbc5riYoHcqgsyCaofxcE/p1M0ngl0eH8eyF+RIeNwq2hVWtZcNECJBKNSsP8rPn8YdUfGBY5rO3B3YBbKjYwldsFjhoI7XjBL62jGpvWPzd5o1rFFbHhvJVXTLHDSZQfo8illEhkr1IZdgftulopZQWwHJgF3AB86T30GdDEON6A2cAWKWVhw0YhxA3AecC1XpUYUkq7lLLU+/dmIAvo3551no7YaxVtoSEoCCk9rP38Y0KimiYvLM09UfNZqzeQW2Xnp3U7mp1T1H/Yc1ZBZR57a2ykGvWYvLaGwlrFgye+jQjujlJnc6IvtmOPMaDVtrwr+t2L7/D4448TIxXf/sF9274hWWKVOIKK4s7XrfYJYyj8erFS32Pxn7rsNGqV+rhKqMquPEuFGTrnsuovsiuVFP4RIQmKYby2tMNzGZ3V2HT+2x1cnxCBS8Inx8r8Ml+5rZxKeyXPbXyOKxZcgdVpbXvQaUSbOw4hRBTglFJWCCGMKLuKZ1FsGmeiCJLpwIFWprmak9RUQohZKMbwM6WU1gbtUUCZlNIthOgLpAP+9XM8BbHVCw5zENLjoaKw+YSDqz/9gOHnzsUYpHzp7vvfSo5ootk+fCAWc2MBIBv+tftL9qpmMDjohAdWkVt5gow2+N+2cSSvmo9e3UqwFKSNb1mVVFFTh7kq5/jrG2+726f5Q2OUHUxFuX9uFD4RNUCpObHxP0r98g5ETLcHs1axC1XaK3tceJTbynl6w9MATHapwe2AxDEdns/orMFhjvLX8uhnMnBGqJkP8ku5Ozm62XLDUkqEEFRX78HpLCc8vPFO1e22olIZEELFXT/exa7SXWiEBpd08fym5/njhD+iEiqklFQ7q3l6/dNc1O8ixsWOw+Vx4fA4cLiVH6fHicPjwOl2Km0eb5tbaYsNimVwxGC/Xb+/8UVVFQe867VzqIBPpZQLhRAVwMtCCA1gw2tvEELEA/+VUs7xvjYBM4HbT5r3X4AeWOL1dFgnpbwDmAo8IYRwAW7gDillN377eyf1gkOj1+N2uZh89Q2s+vhdAPqPn0Rs+gB+/kCpNVFVVHhccBzRKIbu4U8u5Y5+bh655YIGswqqXEZCkgfh3vUlOelTuCzmhN/8PquNSAcU7CzD6XCj1XU+UynA/oNlfPPiVgwe8AwN5bxzmnpF1fO/b34CoN/4s7lu9mSfz2E0GjEIJ+XV3fwkOOYm2PIuvHaGksdKpYVJv1HcUv3s0dMnRAk8PFpzlBRLil/nbi85DYR76v7FoDFC/3M7PF+Qs5pSXZofVnaCX8VHcueew3yQX0qwRk2Vy02UToNTSn67N5taj4ql8QsoyHvn+Jg+ybfjdls5mvf+8TarB3aVnrC+zOwzk88yP+PHwz8qjgENWHhoYYfWqlPpWHnVSkxa/zpa+AtfvKp2ACObaV8FjG6mPR+Y0+C1FWjiPyql7NfC+b4AvmhrXb806mMyXr/12kbtQ6adw7l3KGk16gWHJbrpE7zebeeT/Q4eOaldAKRNR7XqJQx9bWQEndiV2DwegvRqXHY3tRV2QqP98yH+/O3dmCRMuXMIo4c3VbfV43S5Kcjchkpt5NpZ7bdThOncVHRR7EmLRKbDdV/Clveg9KASOT3vWhjza5j9LKj9t3tbfnQ5ALGmnveoSgpW1IfRLhe6nCUw9pYOp18BMLtqKNT715A9J8pChFbD/2U2F82uqGdz8j6h4b78SO7/EEKNWh2E2608vB1xKH2vSp3M8MS5zEieQV9LX3Kqcii2FrOlaAtXDriSA+UHGBQxiCBtEDq1Dp1Kh1atRavSHn+tUys/WtWJ9qyKLP685s+sP7aeacnT/Po/8BeBlCOnCPHpA5ptD444Efw29oJLqauuwhCkeLdIKQmVVvoYHCSFGlhUYMRpt6PVN0xdISFhNEK6GVxzkEFBJ54R0owGfvL6JNhq/WMg/3H1EUJLncgMS6tCA+CDH9ZhEk6i+w/vkP99qElDUblWSVTUnf77yeOVHwC3Cxb/UUntrjMpKccdVtj8DhTsBLVG8UCK6AdXfayUovUBp9vJJ/s+YU7qHPqFNfsM1q38YdUfAPhN6HDIXQSjftWp+UxuG061f21repWKBaPSya6zo1cJzGo1aqGUlz1r437ONWQx94zduN12VCodIE/YARuwYdnVwC5uH/MEkSZFnXbPyLbLBvvKoPBBPL3haVbnrw4IjgCdI3XkGG7+x7+x1dTw84dvc3TvLgBstdXH+0y99qZGY1btOESFMHFlvBqbB9xCQ2mtHVthGX+Zt5brq9SMEholeA0YV7uf5Aa1mYM1KpyAW4CtpvOCw+X28POXWYSq4JYbh7bZPycnGwH8+pKOqTzCQoLILAdPXTkqUw9lmFVrYPYz4KqDNa8oQYLF+8FRDcZwpbRqUBQc/BGObYOk1nxMTrCrdBd1rjpm9ulkPRE/UZ8Gfo7b+/k5sBhih3R4vhpNEGpHddsd20lfk56+psY5vw5482mNCFba1cdzgjX/sLGmKJM+BsNxoeFvtGot4+PGsypv1XG7S28jIDhOIey1tcx77GHcbjcjzp3LGZddg97Usvro7SXb0XhU/GrOBP764TJUUktosIkbnp/Hek8852kMOFVaCI6lVB/B5LqsRkbDeu8qp0Zg98OOY+3GY8TUSoLGRRIS0jRh3+HCMl5+4y0MBgPpGUOh6CBWbQj6DrpPhoZH4j5soyb/ACH9xnd2+SAlhZsXsuanRRS7TNzyyHNtxpMcZ9azYI5WPNgGXwQjrlFiPwAqcuEfQyBvi8+CY/2x9QgEY2PHduxa/ExpXSkpISlo6ryZlYdc2qn5qvVhaGqb1qLvCjYVZwEWxoT75sygEwJ3F7vfTkmYwvLc5eRU5ZBqadkG2FMEBMcpgsNWx3f/eh6TJYyrHn+WkKjWa2PU1tlZXSwYrq8kISmeg9WCWFHDzm272OSK5uwESaqwIMoE+ZWlRDkqsIQ3zhNl9N4UHRqw1Xa+9GZJvqIjbqmux1uv/ZMQAdTVcnjzcoSAfgM77lkSFhMPHKXiWHanBIfLWkXO6i9Yu3EzWY5IIBoDbddqb4TWANP/0Pyx0CQlyjpnJUy4w6fpVuatZGD4wF6Rbn1V3ipW56/mooRpsOsTGHurUl2xE5SHpBBVvt9PK2ydvdWlgIXhkc2rg08mPSSexUXZON1OtH60WTVkUoJi01uVt6pXCo5fVtTKKcwPr/2DioICZt/9QJtCA+CDb1djFzqunqjov8tdavIJ4ZrPclAJePSKSSAEAknm7sVopZuojFmN5jB7dxw2gxprtaPJOdpLdY0yR7h3t+FwunB7XX6PFBQdL7ozesYFpI2fycxLruW2yzrumRMamwJAeUlh6x1bQHo87P723zz/3FN8sPowBc4gpg8Mw6Kykmhy+r7b8IXUKcpuxNO2MX9XyS52FO/g/DTfa5x3Jd8e+haAkWozuO3KtXQSe3g6CdY86my1nZ6rLbLqPERSToiPxvzBkUNxSthTuLrL1pQQlECqJZXVeV13js4Q2HGcAhRmZ5G5fjVnXHYNSYN9ixL+Yks+IR4dF81UbrxXDQ5mwd4KYsySB84fRVpcGJsQSEB/WElNUlRXR3IDQ/L40CBUwL5BJsbl1bRwJt85vLcMs5D0TQhm+Za9LP7mc3TCjV0bhNAHoQOsYf04f8qoTp8LIDTOGwRYUdmucdLlZMl//8K+IgdlniBitS7OGNOPjGlXIF0Olj33HCMi/PzMlXqmUjGvYKcSed4KK46uQC3UXNzvYv+uoYMMjhjMwkMLmRnmtWlYOh4xXo8pZQLqna9zYN9yho3oWIZdXznsNJKkqWq7o5cR8WfB7vlsPbac4fFnddm6JsVP4rPMz7C5bBg0XROE21ECguMUYMPXn6EzGhk154K2OwPbdx8k0x3KpUkuNBrlLX7oV3N46KR+AhBINAmjce//mDHfXINjgRbdI0dAZyLRoOOSmDDmu8uYvq6uU9dw+EglYeUu3ANC0GpULJ8/D513h6F31oCzBo+Ea+ac2anzNESrN6DHjtXWvviTQ8s/YE2BjhSDnTP6hTHqgodR65TAyCO7fkaiIj6pc6qYJiSfofzOXd+m4DhcdZhYcyxBuiD/rqGDVDmUm67ZW3yJ4LhOz9k/Ywa2b3XU7FkIXSg4pJTke8I4x9B8QG1z9AkbhgrJ7pLmMzL4g9yKUiL0ydjddubt/p4bhl/UZefqCAHB0cuprSgnc90qxl54GQazbzeKf8zfhAoz91x8RusdhUBIydipvyY7ZRyp/5uMzuPE7XFSf6v9TZ8YPi8o44cw2SSCsz2sWnkUgWDc9CTe/m4NAM6gWBJS0oiOCCUpJoKhaYmY9L65o/qKGomnHfUrpNvNsnXbCFEZuPa3T6PVNV5PfuY2AOKG+B6M6BOhSRCSAEfWwfiW/9Nuj5uNBRsZGd0ktKrHKKkrIVgbhOqH3yupRoI7H1diMgazIXEmQ7O+obLmKSxBXRMZX+usxYqJOB8+dweLN/LbZXdyuM6GB8GSgtaSZXQMj8fD9V89wfbqrxBCqSHy/LY/9TrBEbBx9GKkx8OmhUr5kgFn+KY3/nHtTpZXmJliqSE1sfU4iYaxDanJQ1k98n4A3A1KlKabDYyvVbEp3cCx8o7tOqSUHNlZQoVaMnZINJk7NwNwx/VXcNdlM7ls2ljGZ/T1u9AAUOHB3Y4UmAd//pSjrjCmDktpIjQA8vPzCRI2QuKbT+feKZLGKzuO1tZXcZCSuhLOSjrL/+fvIFuLtnKHx2sfSD7DbzEzEVPuIdhdy66f/umX+Zojz5tPK0bftpH7oWV3kV1nZ06c8t53RWjpogNb2FHzBZFiFHPj7sNtTcZWOLfXFe8KCI5ezP51q9i04EvSxownOqVvo2Pvfv0zlz/+EXszDx9vq6qu4eGv9hAs63jh9tk+nEF4rRxePIrL7caFfyWv4ODx5ocGJeDQCj7c1f6EgVJKXnh9MyEVboLSQ3jrm58w2ssJ7j+BpJiuj61QCYnH49uXTno8/Lx2IxZRy4g5NzXTQXK0GuKDu8ivPnkCVOVBaVaLXbQq5QZXZC3qmjW0E5vLRnZFFpfm7oXEsXD9V20P8pG09Ilsi53CkB1vUlLZNa65BV71WrS++SqZ9bg9bo7ZbcTp1Dx9zle8PuUP/GXk9X5fz+GKAgDuHHkzz5xzK5PNj2O2Tff7eTpLQHD0YkwhoQAMP3t2kyCgd9bnsrHOwty3tnPVkx/z/cpt3PrC15SqgnhsZgqREaFtn0A0FhyxI5RaWpO2v0LCG6PZsvEz5XVqOEaHh60dyPv03fIcDDsqsUZqqNQfJHf7Gqo1odx9ebMZ+P2OQPr8tHZ4/UJyHSFMGhSPRtfUGFlxaBtlMoTUpC5KXtj/XNCa4Zu7YfXLsPZVcDeOn0kN7sNETRgvb3mZT7/zzXW3KzlafZQMmw2zvQbG3QZt1X5pJxGzHifIZWXPt4/7dd56im1KkGGkofX0JlvylmD1wK/6K3bGyX2v4rJh/+f39RTVKmn5Ei1KQOWRMisVVic+Pvt0GwHB0Ysxh4YCYKtpHEHr8XgodBtI11QxPbyOzdVG7vg2j/W2MC6Jd3DJTN+CyJQdxwnSUkex59LPqFUrT1/ata8CoFKrSLHCPtofy2GrdACSWs1PeAr341ZpuOvGqzFou8e8pgKfBcfKn5djpo6R5/262eOHtq8CoO+wNmxHHSUsBeb8HY6shSV/hh9+D5/dCPu/h8wfwO1CHPiBfxzcwTi7i5cKV1JypGfdNZ0eJxNs3pgWU7iS3sWPJKWMZku/y5iY+REHcrb6de5SWwXP59ZioI4BbXiCLc3+BoFket/OBTa2RbFVERzJFiUqfUCsItB257fPM7CrCRjHezFleUoqhLD4xoF5B7LzsKoMTOtr4Pc3z+VYYSnzf95KWkIUZ08c3qlzZgw9B4YcY933zzJh/dPs27+GgQMmMkil5SuDi1qXi/I1+8hato8J952DPqz1mtD9UkI5ELIMAJdKx6P33Y05uPu8gYTAJ+N4/o5lZNWFMKOfHq2p+WvKPpyLGUF0un/chZtl5LVKVl1rGRTthWV/hX3eDKuxw8BehdGSxB/Pf4dLfriBa368gzdnvEpKn7aLW3UFEklNfTzLB96b6oS7YNbTfjvHgPP/St0/v6Vs/v/huecHn+NniuqqMKo1BJ9UyrewtphPcjbyXrGaAhnBaym1xLSRwn1t4Q5S9WriQru2aFaFvQIpBXHBijNAWpTyXUkO711ZcgOCoxfjsCnGaHHSF2X1DsX+MH6g8pQUFxPB7R1Q/Sjqr2ZuqkIwZOrtVG/6J5VrX4MBExkVauZLTxWrj1SQ+8EhbKpwtP/+kYmPXILH46Fg7V7iJmY0Uql5PB7Wvb4Ve4xigNS6bLz4j5fQOp2kScmoqWfS75yZ/g2kO/lSkEgf9vmrlnyLHi1jz2u+3of0eMiuEqRaPE3eD79Tn4pk4NwTf1flKzuQ2mK45D+kxo3mv0Pv5cZd/2LBppe5t4cER7Qpmo+Dg8gYdx8XlRbAhn/D5nf9KjhCLDFka6IYVb6Zd3dnctPQgc32k9LDztKDzM/P5scKD/vccQRRw2dDoxgZOQC7y8GLuxbw3/JYaoknTpTwZpqbucltu4CX2msYEhLe5XmjKu0VCI8ZjVpR+a3PLmVQXAihJv87jnSGgODoxaQMV55sP3jkN4y98DKmXH0DQgi2ZRejkkYmjOhcYUSPSoOmBd+QIHMYGxKn0S9vJW6Pm7MHRPHHvVUsO1RKH5QP8dacUHJufI9yg7IjOmv/UfqcM4ov/7KMaq03a6/QITwepAoSbDaSoqMpqa7mgNvNvnVrCVm2jFHJSZxx/fXoQ1rfvXQEjQpc7tb9X4ozN7Kn2syUJBWG0OafPIszN1EjjfRNiWz2eJcgBKQ0SCc/YBbUlUOoUv999Ojbid/xKvm1Bd23ppOIMESAEOTp9TDnOSWZ4+5vlAh4P9k7HLUVJNjzWGMZyStHS7hhsKfRw8bPx3bz70O72OkIp4goIIY0cYTrg/byZU0Sj+7Zx0fjYrh8w8/sdqcxSnuUv6SHMzZqus8PLaFaNbl1vgcJdpRaVyVqqXioOVweNh8u56qxyV1+3vYSEBy9GHNoGMYQC3VVlWz85nMsUdEMnzmHfaVOYoQbs6lz0aQOTQjBog6304Fa2/SJxtNnEhGHF/Lf779mX4WahOBk5ntquF2tI9KeT7ku/rjQANi0pgqPeudxoSGkm0GJtahj4sg5nM3Vf/oT2mBFZ1tXWcnWz79gc+Z+lhcXs/aZZ5icksKU227r1DWdjFkLNQ5Pq31Wf/85GjRMuKBlY/Oh7Yotoe/InnmyB0AfrPw0IE5jJr8Lssj6ihCCaFM0hyq8RTpTz1RqkRxaBv384wBRlbONSJxkBo8j3xDEutx8JvZRPnev7fmBJwuiCBLxjNBXcJOliAvi0+gber6ytp0LeKGkDxlrc1ATz9/iS7i5/9x27xwMKkGe3dml+akA6tzV6ITyHu84WoHN6WFC3ybljHqcgODo5Vz7txcBWPjys2z74VuGzpjFEaeRsZbO547SWmIhFwrzsolPaZrgTWtRvpwL7TWsizthOykJUXPTLVOpKatj7dsbmHTTaLKW72drdgSrV9lRA2MHOxhx52zU3tiMk2+3RouFib++mTOkJHPxYn5aupSleXnEL1tG2jT/1SAINuooKW/ZqF9xZA87ynSMjZGYo1o2kB7KzSdMJQnt03Y6+O4kQRfKekfPGk4nxE1gxdEVuD1u1PXZfat8j8RuCenxkPvDP/EcXEYkMCwlA4BFOUeZ2CeR7OpCni4MY4T2CB+OOZNwY9MgwbsHns0Lq5RkiS/3qeayvh0TZlenX8Rj2z/l0x1/59qRv+/wNbVElc1KiMGE3VNNiEaJv1qfrRjKx6X2UEmAVgh4VfVyLNExWKJjGDx1BiW5h1m9YjU2lZ6hiZ3PiqpPUqKPKw42H3RWXXYEgGJdGMnFJ9Qhg2QhwenJxI0fwCVvXE/M+AwyrpyM8Lhwq/VkhB1j9P0XHhcarSGEYMC553L5bbeDEGxZuarT19WQ4CAzNdKAx9l8Nts1334IwBnnXdfiHG6Xi8M1WvqGqbu3IJQPxBujKFarcHoD2XqCzPJMKu2VfLL/E6VULhyPCeoMh+c9TPL6v5BSuhyAWE8twU47G2vtALywfxMeKfjn4IxmhQaASWvk3X4O3u/n5LK+HX8guWjoIyTqNby991Nc7s4/tDXk8WXvMWneeMa/fTFOVRFmjfLdPlhUQ5zFQLi5d9k3ICA4Thn6jVNcQL9boqhMxg3qvN6zz6BxOKQa2+FNzR4PyVtHoS6cLGMS96oc/AcP1+4u4PzfNA1ICk2J4urfDmJAWBGj7vYl+PAE9poaPn7rv6g8HoZN8EPdjAZYQsPwoKbm2MEmx2oKD7OlUDAs3EFoUvMGV4D83auwoyO1b98W+/QUySF98AjBwdyVPbaGQeGDAHhp80tY8aoFnR3LMuBxu/F4PNSVHyNm//uNjoVkTGOQdJKp1lNgrWJ+dRQz9AdID2/d1ndu0jhmJnWubolareXmQVdT6HTz6fanOjXXyRwoV1KXWFUHESoXIbpQALKKa+gX3TvykZ1MQHCcIphDw1Brtewvc6KSbsYNU9IeFFXZyC2z4nC1rsdvDktIEIfUqRiLtzd7PLlgA7n6WBACj8HI+dNG8cI9swhKaN5AHDYwibOfvoqgPu1LcvfDP1+hVK9nbloaA845p93X0RqhkUrepPJjOU2OrV/wNi7UTJ59RatzHNq5AYDU0TP8ujZ/MHnoDWil5Jtd7/TYGp6Y9ATvzX4Pu9vOZ0eWgMHSavR7S3hcDkqfGYrqiTCMLw9Ej43iy74mZ9TvKTrvPcISB9DXno9VZ2DE+kM4pYb7+nZf2dxLhj5IvF7Lm3u/ZNWhT/k552t+v+RKzps3hjU5X3R43rzaw41eXztESZdfbnUQGdS04FlvIGDjOEUQQhAaE0exKwqDcPPbz3ayMaeM0lpl25wWZebvlw9nVHL7ksGVWYYwtHwx0uNGNPCCqXvrPiJthWwwpxNTWc7V5/o5qZ8XR20tu2qqSQVG33CD3+cPi+sD7KS8KJ+G+WxtFUVsOOogI9hBZProVufIzismVu3CHNvztb1PJiyiH2erw5hffZD7aosxtRGP0FWMjB5Jelg6b+9+h1+F9kFUHPF5rMtuJfc/15FaspSGqz8y6E5ShkwjasgJFdP1/eKpLfiSYvowMzaOMXFz/HgVraNWqfnD2N/x6NqnuHPlkwBokLiB7w7MY2KKb8GBNXYbZ354GU6q0HrCcWiziGA871/4LKFGE8He9CeVVicWY9cZ4jtDmzsOIYRBCLFBCLFdCLFbCPG4t32EEGKdEGKbEGKTEKJJuLIQYoD3eP1PlRDifu+xcCHEEiHEAe/vsAbjHhVCHBRC7BdCdLySzylGVfUu9u59lMKi75o9PvueBwnVq7Gi4/vdBZTWOrh0VCK/OqMPWcW1XPLaGl5b3lQl0xrqxJEEYyXv0N7jbXXLv0Cf8x7VtnBMQ+5j+TkT0Blbz+XTUbbPn49Dp2P0+AldMn9ofD9AUlbUOM/WxgX/xY6OyTPPa3W8w1ZHbp2B1Mje+eQHcO2oe6hWCV5aeEOPJsO7btB1lNpKqfI4oR12gIJFfye1ZCkAxZp43L8vhMcqSbmyaSzIqEGTuNi+gNsdX3N3RvcJjXqmpl3Ngot/4DeDzuMPQy9m6aXfkmI0cLDKd0F5sKwAh/owUl2OQavFLNO4f+zNJIVGHBcabo+kyuYipJcKDl92HHZgupSyRgihBVYJIRYBTwCPSykXCSHmAM8BZzUcKKXcD4wAEEKogTygPgvaI8BSKeUzQohHvK8fFkJkAFcBg4F44EchRH8pZVcko+w1OBxlbNlyDW53LfnHPiV6WtP8VDGpaSx4Lg2PR/LOmhycbg83TEzBoFXz0LkDeOjT7Tz3/X4GxAQzY1AbmXG9RKWNgJ2Qf2Arif2UQjyehX9GFSQx3LyQs1I7XrrVF/bt2oXB42HQ7Fltd+4AGnMosZpqVuYGUfaP/2PWlbejC4lkbVYVaUYn8cPOanV87rYVuFHTNz2tS9bnD4YPvpLr9nzEB9ZD2ObN5s8Xf45W3/268Tmpc3h2w7NYbWVYLL7b4Dwlh3ChRj6aRxgSdTN5wupRvhODUKu34nY7UXeha2xLhJvjuWXcCaEWrg+h0JsqxBfsLsVx4KKEB3ny7Bub7VNtU/qE9lLB0eaOQyrUl3/Ten+k96c+YssCtJU6dQaQJaWsV+hdCLzr/ftd4KIG7Z9IKe1SymzgIOBr8qVTloMHn8btPlEmszU/c5VKcPPkVG4/Mw2DVlEvhRi0/PPqkWTEhfDgZ9s5VumbcTIuTXGz9RSd2HFgDMbjFmi7WGh4PB7yPR4SVCrUzaQw9xfX33Y/ExNgX4WOt/7zBus+/xdWDEw5s20vm0N7t6DCTZ9RM7tsff7g/y75gjvN/fnansfDn87ye84oXzBoDPS19AWHFbS+71A9DitOtGj1RjT6tlNrhIdNQq12cCT3584s129YdEHUtBFk2hCbU9mN6TQtC4XKOkVw9NYdh0/GcSGEWgixDSgClkgp1wP3A38XQuQCzwOPtjHNVcDHDV7HSCmPAXh/1xfSTgByG/Q76m07bSkuXsyxgi8x6DuXddWgVfOva0Zid3p4dtE+pJTU2ltPTGgMDsMlVUzIfpW6ZV8CIE0xqNQS6eq8S2VrFO3dS51eT5+Ern17zdHJzLz1Ca6fM5Fyj5mfsp0k6aroM65tVcehggoStZXownv3R1CoNdx12RfcGzaKJZ5KVqx9vtvX8PXBr9lVuosgjxuMoT6NKfr6j6QULUaNG3udb/XF09LmICXk5v7QidX6j1BdCDVuibsF9Zy9robFs8Yw/64L2Lb5O2ps1egdEr2qZaFgcyrOLkatf7MN+wufBIeU0i2lHAEkAuOEEEOAO4EHpJRJwAPAWy2NF0LogAuAz3w4XXOP2k0en4QQt3ltK5uKi7smV393kZ3zGiZTGnHxlwMwZrQv/6bm6RsVxA0TU/h6Wz4XvrqawX/5gVUHSlrs73Z70HgrjWkX/5q6pR8jvKVAaSH2wV8UZWYCENe/c6lTfKXPuDmM9Tp8TT5jXJs5p6zVlRyzG+nbS10im+OmWa+T5lHxu/3vsnL9y91yTikl9y+7nz+t/hMjokYQ5HYpnlU+EL3tFQB0OKnYvdSnMVFRqdjqoqmtbd6NvLsJNYTiQlBtK2z2ePbO1STl1JL+0wH01z5I30tu4v0X3Ix+7HWKC5qPv3G6vd9Jde+KG6qnXe64UsoKYDkwC7gB+NJ76DNaVyfNBrZIKRv+ZwuFEHEA3t/1lWmOAg1DeBNpRg0mpXxTSjlGSjkmKqpnPEn8gdWaTXX1ThLir0SnU9xcdbrOXc8DM9MZmmDhcKlSP+O6t9bz3Pf7GP3kEv659AAHCqvZ4I1K/XLDMha4J3BQFYuUWowr78Ast2B19kMYW69R0FkcdYpg0gf7P0dVS8y6+ffceP4UBky7qs2+OVt+AgR9B/SuaPHW0OpMvDb7HYIlPLvnLdyerjcNrsxbydIjyk3/71OfRXhcJwIBW8PV+Ald1UZNjIZotMPQaI5gt/d8uvEwg5ISpLg2t1F74eG9LLzvEvL+9EcArH+8g/LfXktBPyUSPD6/gKxzz+WrP75IXW1j1fJxwaHpnRETvnhVRQkhQr1/G4GzgX0oN/P6tJLTgdYK8F5NYzUVwHwU4YP39zcN2q8SQuiFEKlAOrChzSs5RSkoXAAIomPmEhqqBCnl5X+C223v8Jx6jZrP7zyDDX+YQUyI4g302vIsSmsdvLgkk5kv/cwV/15LhdXB97tyeYRfU9QvBs+131BXG02tKwP977peDeB2Kqowlab7tuNqrY4UH+MxsneuA6C8qoa9P/yPVR88Q+bSD3Bauz7ZXWeIjx3JQ0mzOKySbM78ukvPVWYrO56n6slJTxKrDwfpAV3btgrrjhNrK9ImET5gos/njYk+C5VKkpX1fbvX7G8ijIqWvdTa+Pl226dvkLZ4L/GHayiK0jHi6ruZeNsfmbZwNYP27UXzn3epjEth4Of/Yc05F5C779DxsU5vvWNtV2di7iC+rCoOWCaE2AFsRLFxLARuBV4QQmwHngJuAxBCxAshjvuTCiFMwExO7E7qeQaYKYQ44D3+DICUcjfwKbAH+B64+1T2qJLSQ13d0eOCwGrNxmYv8B6TFBYuIDR0HAZ9LGZTP6Kj53L48BssX5HB0p/SKCtfS0HBfOz29pUK1WvU6DVqnr10GBP6nsh1YzGeuEk/+vly1h8xMyxyN0Ys6IeegfHvBzD/dS3q0K7PAhuVrGwsc3fv7vJzdYRgb7berzblMW/tEX48aOOjlQd56bmnWPHB37HVVPTsAlth8sjbUEnJpv3+K+V6Mg63gzPnnckLm18gzhzHBWkXQL2Xk7sF25q9GuenNyO/f5Tqw0rg6ebRLxD5yDbUWt9dnvv3n43breZYgW/qra4kwqgEmZZZC9lTsBKHS9k9OA6dEATWqSNQqxs7saZPGcc5iz6j5JG/Yakq4ehVV7H2M0UQ9nZVVZvuuFLKHcDIZtpXAU0ip6SU+cCcBq+tQJP0jlLKUhRPq+bO+Tfgb22trbdTXbOP7dtvwW4/hlYbQUjIUEq9eXfS0/9IqGUMVushkpNuBhRPqsEZLxITPZedu+4BPGzdquRQio+7gkGDFBfA3bsfBCSDBj1DccmPFBV+h0c6GND/cQyGxlHbZw2I5sz+UXy5JRd77ToSxYs4HMXcsuSfLNrjBExMTliPztO+aG9/0GfSJEIXfsvqffsYY7ej1feuWImpv/oDE2vLKMnciMOjIrLvMPJ3rWL9mpUsO1jLuuefZUKKifEX34HB0rvUpcGR/Rkg1WyuyOyyc2RXZh//+8oBV6ISKsVCKdQtxnE41ryBbo8SZR054R4AtEXbUanbF4tsNodityeh0TSf9aA7CTcp352Xtv+HAoeb29NncM/Ef6A9euJhz9y/+ZQ2Qgim3HgJBzP6kXfPPUT/6QEW/Pe/5F14HaA9dVVVATqG01nJzh13IqWTvn1/i1ptorJyKwkJ16DTRXHkyH85ePAZVCoD0dFzj49TqTRER5/LqJEfotVGoPEmPKuo3MyhQ/9gw8aLKCj8moLCb9ix8y527bqXouJFlJQsZfWayRzJffv4XLW1h6is2o4Qgonxm4hxPYrTWczwYa/z1c1ORkTt4P9GrWZg+AGCI7q2sllzqHU6ZowaSY1Ox0//frPbz+8LGnM4sSPPJXn0TExhMfSbcinXPvwPbj1vHAkmB8ty3Pz3n89SfmRfTy+1CcMMMez1tL9OvK8cqjzxRH1OnwapYtTaFpMcqo9tAcCBFvW6f1GFGe3o6zt0fpNpDDpdCZWVh9vu3IXEhaShRlLhVBQjm4uUErfBhTXH+ySPOavVOfqNG8bYpYs4fMUtRBcc5sx/Psq3X/8Ora1jOb+6moDg6AKklOzZ+3/Y7McYNvR1UlPuZtLE5Zw5dQsDBzzJsKGvYbcXUF6xjsTE69FqmxqHw8LGMXXKBs6cuoWMjBewWrPIznmF6uqdx/uUliolWceP/54xY74EBAcO/BWns5Ly8vWsWz+TTZsuIfPA38g9+h4AI4a/Q1TUOYzsfxFfP/goM/TKhzs8rWniwu5gyDXXkGytY2NRIRVHfI++7WkSxszhuv97gRtmDqPGreWtt9+mNHNjTy+rEZagOGoFyJqu8TocG3sicWCjGhVqHbhPEhwOK+59i1BnKlrsrzmHHBJZk3A7A4d3LAFhYoKSIv3gwYUdGu8vLMYYPjr3NZZcvoRJ4THsrCrj3PeGE1rlIfviDGKWLSJlcNt16k3BZmY98SBDf/6J6vAYVEiSPTVtjusJAoKjCygq+o6Skh/pl/Y7LJam9aktllEM6P8ERkMyfVMfaHO+2JjzMRpTjr+Oj7+KUIvyZYuLu4wgczqWkOGMHj0PgNLSFeQeffd4/9zc/1FdvZP+/R8jImJKo7mrqnehqlJh7tP9Ow5Qtuqzr70Gt0rF4nffbXtALyN10iXcfMX5eBC8+/FnVB071PagbiI8NBWPEOTk/NQ18xtO2M5izbEnDqi1x1VVVUtfpPa9q3H9PR31J4onWx4xqIdcQvHcdzjr+oc7XI61b98zcToNlJT2XGbgejJipxJqjGVmn1mY1SoGWbWogP6DRhAel9KuuUyWYNJ+rezCzDG9SwVaT0Bw+BmXq5rMA08SHDyEpKQbW+yXmHgtEycuQ61uW68vhJqJZyxl2ln7mDE9i0ED/8bw4W8xoP8TpPf7w/F+QWZFj7p33+8pLV1GWOgEBvR/Ar0+jpEj3iMpsbFKwOPxUGs6iqkmqstrKbdG3PDhpHs8ZNrsuF2tByz2RqIzJnL9xedSJ7V8+c6/8HRx4KSvnDv4OjRSMm/Ph36fu8JWwfD3hrfcQXqgKp+QlY9jPvQdGueJJ+f3uYRp06czduxYDIaOV7HUanW4XP2Qck+P5uhqyKXDHmLFtdt5IPIiACL6j+nQPOpQJXWfp9a3oMjuJiA4/MyR3HdwOIoZOOBJlPRc/kPVwDdeozGTmHhtIzWXRmMmKekmPJ46VCo9gwY9Q2LitUyetIrw8ElN5qs6tBp3qJswc89ndImMjMSl1eCs6t2uri0RN2wac0b1IccewuqPnuvp5QAQGd6PWZpwvqo9RLXVv4We3t/7fvMHpARbFehDwKXE6fzEGbyt//XxLmeElhIe7p+qdhbLGWi1tRw7ttkv8/kL22ElOsHcr/XMyy2hDlMEh7u8wl9L8isBweFHamoyOXLkP0RFnUNISM+oftL6/pZ+/R5h1MgPMRpbLoUKcGyrohqKHd5y9bvuQtQ/Map7Z4oFXxhx/q1khFhZdshG3rauUQ+1l/P7X45VJdi9++Qwqs5hc53IKpAeln7iQE2RYhi3JFKfBKJObSFj6kXUoMR2CB/TkfhCaopSNCw7p/mM0j2Fc38OnmAV2oiOqZrqdxoqU9dkpe4sAcHhR/LyP8LjcdI//U89tga12kSf5FsJDm47QWGJex36IhMhfTu2nfYr9UniNKduiRghBOff9FvM2Fm8eHFPLwcAS6SivrT5ecdxy9BbSAhK4NyUczlQfuCEa259HY7QZFAp7+Vc9/cMW3IZy0MuZUHor0mdfa/f1pGQMAy7PYSqqt7lmEBhLTLB1GEVsDAoKuyAquoXgF4Xg5QONJquTdXhD8r3LccRXUektmsKNLUbr+CQvTRS1leMYbGMj5ccthooL24+d1G3rsdrQ7P5oQZ4Q8IMYXx/6fc8Mu4RNCoNn+7/VDlQ4XWNLcum/IdnTqwDO2kWD+ff/yJJyZ0ve1yPEAIhBqJSZeFuKeiwJzBqEXm17Pu/i3DWVbd/vPf7IPQdtwF1Jaf2t7SXYTYrFeJqrb3Hs6Yljmx+BTyQOP6enl4KoNSahtbTyZ8qZIxRPNf2rV3UwysBg9cuVtdFyRcijZGcmXgmi3MW45EeiPQmrPz+YcL2ftCoryuuqYehPwgPPwONxs7hw6u6ZP6OIHQaVNUSOX8/Oa/9tt3jPTZFFagyBgTHac9xwVHbWtqunsdReoxSy3aCC+MJSuzamhu+UulwoHU60fey6PGOED5sFtGijH2ZXRe17St6oaiLbHRd1p7RMaMpqiui0l4JccPg0TxIVNzFNzGUQyQx33wNA87+VZecv29fpQjY0aPLumT+jqDtn3r8b9d/VpE3/5/tGi+0isCXvdTLMCA4/IjBkIQQOqy1WT29lFbJ+uHPSJMkZWj7n4S6ipK6OsKcrtNix4FGx8AoDUdqNNRW9Wz21rLaYwBoRdfZjvRedZjL473J6YPgqo85ZB5FVvzFhP1mJXMeeAVdFxXriorsj8MRQnXNli6ZvyP0/+N7DNizm8RFiit0XWb78rEJb5En6QwIjlOO6tV52DLLfe6vUmkwm1KprW1f3e/uxFVTSaF+OYYSC1FDL+rp5QCQ+cMPFHVRTfOeYtDwcUhU7O9BdZW1ppD/W/NnzB4PE9Iv7LLz5FTlAA0EB0BQFOsSbiW/VoXFYkHTxU4PKjEAtfoQrl4SQwOgUqkwJgxEaiS2xeuxFeX4PFZolf+XdPae62lIQHC0gONYLZULDlH2WSYeh+/bfJO5X68WHNmLHsMd5iGlz1294ul+x4cf8skqRTedGtX1GXm7i9gx52Ohmn17dvXYGt744S6yhIsXM24jMXF8l50nxqTUt28kOIBhw4ZRWVlJVlbX78DDwiei1do4dKjns+U2RKMzYbzvAlR5NrLu893tXdQL2l4kCBsSEBwtULXkMGhUeKod1K475vM4szmdOlsubnfXVs/rCB57HfnyO3SlZuLH/brtAV3Mrs8+45t9+wh1uXno3nuZ/bvf9fSS/IbQmxkY5iarUmC3dr9LZWHRbj6q2s/52mgmjv9Nl55rZp+ZaFVaXtv+WqP2gQMHYjab2bix611lBw28Eo9HzaHsFgITe5DU255Dc9VoVNtKqcne6tOYgI3jFMSeU4ltTykh05PQp4dSvSIXj823N9BsTgMk1l7oWZXzw1O4Il0kR9/Y47uNvd98w1fbtxPscnHTbx8gKKJJ5v1TnkEjJ+BGTeaKed1+7jeW/R9uAXdN/WuXnys+KJ5J8ZPYV9Y4Q7BGo2HEiBEcOHCAmpquTdYXHByD230GavUGiop7X6bi6CvuAKDwc9+yQNfvOAKC4xSiclEOqmAdQZMTsJybgqfORdkn+5GetvPhmE31nlW9S13lcTk5av0cbZmepCld+wTaFpmLFvHFxo0EuVzc9JvfEHwKl/5tjeSJlxKqqmX91t3dmkupqHAnX9Ud5gp9IglJvlfV6wxhhjAOVhxk3r7GQnLYsGFIKcnsBg+zkSMeAWDb1mfa6Nn9WPpPxpVuoO77Nb59FuoFR8A4fmrgyKvBcbiKkLMSUenU6BKDCT0vDdu+Mqyb2g7oMplSEELd61xyc5e+hDPaQWLwlahUPZfW48CSJXy6ahVGl4sb77kHS2xs24NOUVQaLRMHxHDUEcyRrd2XguT7zf/CLQRXTfx9t50zwqjsGP+6/q8syFpw/OYYFRWFwWAgLy+vy9cQEzMIt2scQrWakpLe9eAmhMA4bRzqPAdVe9uONznhVRWwcZwS1O0sBpXANDL6eJv5jDh0ycFULT2CdLf+tKBS6TAa+1Br7T0uuR6Ph9zS99FUaOgz45EeW8emDz7gkxUrMLrc3HTHHYQlJvbYWrqLEbNvwEgda1b82G3n/LZoExluFal9pnbbOW8fdjuvzngVgN+v+j3D3hvGvH3zqHHVoO7G/GPDhz8KSLZsebrbzukL2TvmY/vkZwCMCf3b7C909TaOgOA4JXDkVqONN6MynchEK4Qg+Kwk3JV26va0nfPH3Ms8q46t+g/2WCvxmvNRa3omwG75v/7FwoMHCXM6ueW+ewlPSemRdXQ3upAoxsTC/kodZfnZbQ/oJKWlB9kjHJwTPrTLz9UQg8ZAtCm6Udtf1/+VyR9Npra2lqCgoG5ZR1zcEFyusQixktKyrv9/+8r+g0/g0UlcWjWa4LZVsye8qgKqqlMCd4UdTVjTMH/DwHDUoXpq1+a3OYfZ1I+6uhw8nubrLnc3h/PeRF2pIvWcx3rk/Lnr1/NzYSGJdjt3PPYYloSEHllHTzH27MsQSDb90PVG8uwsJW5kYJ9pXX6uk9Gpmwb46d3Kg0p3CQ6AEcMfRag8bN7cO3YdLpcdTUg5xwYZ0DjdrJg9s0kfj8dD3pIf2P/qK0DAOH5K4bG5cJXa0MaamxwTKoF5Qhz2Q5U4C1t3rzSb05HSjdWa00Ur9Z3SPYuoi6kg2j4ZjaH7vrwNWfTNN2hdLi6/9140p1mgny+E9BvLQFMFW49U4qjo2sSHh4qV0sIpyVPa6Ol/+lr68tJZLx1//dqM1zC6lffbbG76neoq4uKG4XSOAlZQXt6z9cgBig5vQ6jAcpGSGiX2cD55S5TsySXr17P1rjvYNnYMVffej+eV16jKPoTwpt6pz1nV22hTcAghDEKIDUKI7UKI3UKIx73tI4QQ64QQ24QQm4QQzVYDEkKECiE+F0LsE0LsFUKc4W2f5x27TQiRI4TY5m1PEULUNTj2hh+vt1UcuUoWS11S89ltzWNjQSOoWdt6XIfikkuvsHNkb3seYYO+Mx7vsTV4nE6CPR4s8fE9toaeZsK0udiklk9efxqntQPZUn1ka/l+wjyS+PD0tjt3AdOSpnFxv4t5bupzHKw4SJw1DiEESUmt14bxN8OGPoJK5WLTJv96WJWXr+fgwWexWrM5dOgflJdvaHS8tGwVDkdjdXZlmaK2DksYgvZ3DwJQ9uBDbBkzmuIbbkT/0wrcZjM18YqjiMpoRBWk3IPcpf5Nh+8vfNlx2IHpUsrhwAhglhBiAvAc8LiUcgTwZ+/r5ngZ+F5KORAYDuwFkFJeKaUc4R3/BfBlgzFZ9ceklHe0/7I6hj2nCgTo+jQvONRmLabh0Vi3FLYa12Ey9QVEj9s5agv3UxmVQ1jxQAxR/ktl3V4iTSYqtFo8Hk+PraGn6TP2XC4c24dDdgvvvPQnSvev8/s5cg+v5EdnKdON8T0Wp6NWqXli0hPMTp3NzpKdRHuiiYyMJDi4e0sNJCSMwukcgeQnystz/TJnTs7rbNl6LYePvMnadWeTnfMK27bfdFxQFBcvZtu2G8g69EKjccZgpZpfTUU+Kddcg0utQuNw4jabsF84l7ivPmfYggUQHIQHMEZFoTKb0ERHY9vf84kym6NNwSEV6qN3tN4f6f2pr1tqAZoo/4UQIcBU4C3vXA4pZcVJfQRwBeDfEmUdwJFdiTbWjErfcl4d87hYpMODbX/LOazUaiNGQ1KPu+QeWvk4COg7/tEeXUdMbCwujYaSXpAtticZMffXXDFlAKVOI298PJ9N855D+kmYLlnzLNf/dCd64Napf/PLnJ2hylHFyqMrCXGHEBoa2iNrGDrkYWXXsblzuw4pJYey/0nWoeeJiZ7LyBHvERd7Kf3T/4THYyMv72M8Hif7M5VdvTjptpqUPh3pFhQXrEVjNJH6/SL6rl/L2BUrGfHs84QNGsyu+39D0P6D2IPNqNUahBAYMjJwZPceA39DfLJxCCHUXlVSEbBESrkeuB/4uxAiF3geaO7u1BcoBt4WQmwVQvxXCHGysnMKUCilbHiXTfX2XyGE6BZlreNoNfbsSgwDW6+FrEsMRuhU2HNaz3pqMqdh7UFVlaOunGLjBoKORmEZ2LPFmuIHDADg6I4dPbqO3kDGjGu48447SDS5WLjXys/vd96Au33XxzyU+T6xUs1/Jz1DQvxYP6y0Y9S56nhj+xtM+ngSNrcNg8tASEhI2wO7gMTEcTidw/B4llJZ6XvaoJMpKPiK7OyXiYiYRkbG3wkPn0RGxnMkJd1IRPhUjuS+TU7Oq9jtBQC43dZG49VqA566cNwqJZYlKCkZvSW0UR/PIUVAWO6773ibJiYGV3Fxh9fdlfgkOKSUbq9KKREYJ4QYAtwJPCClTAIewLurOAkNMAp4XUo5EqgFTg4kuJrGu41jQLK3/2+Bj7w7l0YIIW7z2lY2FXfynytdHso+y0QVrCN4auuxBUIt0CWHYD/UuuAwm/thtR7C4+kZr4icJU8ijZLkvt2m6WuRhJEjQUqO5eT09FJ6BZbYPlz/4NMMDa1lWbaDo1uWdGq+r7e/iUHCW5d/z8D+5/lple1nb+leJn88mVe3vcqAsAE8dsZjeBweTCZTj61pcMbv0GicbNzYPgFdWbWdgwefJfPAX9m3/w+EWsYyfNi/Uakae4717/9nhBBk57xCcPAQgoIycLoa3xvqaktRB5Wi1tIiqtpaalKTSbv+RM0STUQ47ooKpLvraql0lHZ5VXnVTMuBWcANnLBLfAY0Zxw/Chz17lAAPkcRJAAIITTAJcBxP0UppV1KWer9ezOQBTSJmJFSvimlHCOlHBPVyZQVVctycRVaCbskHZWx7fTPhvQwXIVWXBUtezyYzf3weBzYbP7Rr7YHj8vJMcd36AtMxEzumuI57cEQFkaIzUZhue8p6k93VGo15938CGZh4/sffuhwShLpcrDSXsQZ+ijMwXF+XqXvHCg/wE0/3ES4MZyXp73M5xd8zuzE2Ugpe1RwJCdPxOEYjNuzhMrKtl3p68nOfpnDR94kN/dtwsOnMnToqwjRNJDRZEpl7JivSEq6mWHD/o1OG4bT2VhwbF/7PABaVUqL55NCwElqS3V4BHg8uCt7tqZLc/jiVRUlhAj1/m0Ezgb2odg0zvR2mw40UehLKQuAXCHEAG/TDGBPgy5nA/uklEdPOp/a+3dfIB3osoyBzpI6qpflYsiIwNiGmqoewyCln21vWYt9zGbFq6UnDOS5P76AK9xJQuTVqHpJDe8YrZZCIfD0Ur/0nkAfEsGMjCiO2oPYv/LrDs2RufdzCtUqpiZ2X5R4czy57kn0aj3vz36f6cnTAaj03vC60xW3OQZnPIJa7WDjpid96i+lm6qqnYSHT+GsM/cwfNi/0elaTsJpNCbRP/0PGPSxaLQWXCftOKqqlYy4sXGzW5zDEx6OprCokQOJJkK5z/RGzypf7ipxwDIhxA5gI4qNYyFwK/CCEGI78BRwG4AQIl4I8V2D8fcCH3rHj/D2recqmhrFpwI7vPN+DtwhpWz5Dt1Jan4+CipB2EVpPo/RRBnRRBux7ihpsY/Z1BeA2m6uBujxeDhS/gGaCi3J0x/q1nO3Rlr//th1OnKWL+/ppfQqhl9wJxZRw/KfV+K0VrV7/Kd7P0IrJWcO77k0+YerDrO1aCs3D7mZWPOJ3GPV1YrbscHQs3WzlV3HSKRcSmlp2w9yFZVbcDrLiI+/ArW6fZkWtBpLkx2HRqvMERKW2twQpU+fJAw2B6VbNh9vU4cpgsNV2mW3vw7ji1fVDinlSCnlMCnlECnlE972VVLK0VLK4VLK8V61ElLKfCnlnAbjt3lVSsOklBdJKcsbHLtRSvnGSef7Qko52DvvKCnlAv9dblMc+TXoU0JQh/j+ARFCYBwahSOnEndV89HhGk0wen0stdbu9azKXfwqjpg64lXno9Z0TanOjpBx7rkArFyxgl2LF2Ovav9N8nRErTcza9IoClzBfP7aE7iddp/HllUe4RtrDhdoIoiw9Jy79Y+HlTxc56ac26i9tlYJlI3oBSnzR454DJBs3PTnNvsWF/+AEDoiwtu/i9NoQ3G5KhupHlVSEQCVZftbHCetdYqbalq/E3NF1AuOlh9Qe4qured4CiCdHlSW9idhMw2LpHrpEep2lRA0sfnANrM5vVtVVR6Ph6O2b9C4I9CvnELexo+Rbg+4QHoEQq0GtQNdcijh101DE9J9keQhMTHEOBxk63Rkr1lD7JIl3Pq3v6HuojrUpxKDzr6G2aUvsGivYP6/HuXCe55BpW37/zLv5z9jF4JfjevZneXRmqOEG8Ib7TaklKxfvx6LxYLFYunB1SnExQ1h564z0WiWc/jwGvr0aT7dvJRuCgu/JSJiKhpN+78fWk0IUrpwu61oNIqKzuEqQA0Eh6a0OE5oNAigaP06kmYpKi211435lLRxnPa4JWja/2/QxpjRxJiw7mjZo6ves0rK7gl8K9z9A7aQbKKrzkO4a/DYwpDOCKQ7GKQBj1ODdMfhOBLKscdXUbu5ewveXHXHHZyfnMwYnY4Cs5mlr7zSrefvzYy/8kGm9TOxvTKERW/+BdowljvtNXxevIlJGOnb//xuWmXz1DpqSatMY/Hixdi8KTJ2795NQUEBY8eO7fJ6474yftwTuN1adu1uOYtCefk6HI4iYmMv6tA5NFpFSDa0c7g8ihtuUvqMFsel/va3ABR+ckJzL7y2od4oOHrHO9qDeGwuVPqOpX02DY2kaukR3JV21Jamqi6zKQ2324rNdgyjsWsT+3k8HnKOvIJGFUH/S3+H+urmc0J5PB5KXv0OR54FZ34pjO7SZTUiLDmZ0TffjJSS3D/8gd3HjnFO952+13Pmdf+H7c2/sDZfT58lHzDknOtb7Hvo4LcUqQUP9O1ZoQFQ7awmOT+ZNflrqKurY+jQoXz++ecA9O/fdgrx7sJiicdguAyX6yN27vyEoUOvatKnoOBr1OogIiOmd+gcWk0oAA57KXs2fExt7T70YbVUHTGjUrV8uw0bNoJDIUGotu9k4zkzUZeVoa+tQwXkbttCbyt19ovfcXhsLoQPLrjNYRwWBRKsu5rXQR73rOoGO0fRnh+xGveTGHQDal3LiQRVKhV4P8D69J4poiSEIDI4GJtG062V8U4Fzr7pD8SoKlixcVer/5vqugoAIkK6NwdUc7g8LmqDFXvG1q1bee+99zAYDNx6661ER0e3Mbp7OWPCo9jtFnKPvojL1dg+6XbbKCpeTHT0rHYbxevRaJWQsx2b/kq56zUceqWAV/8MH6puDs5AZ7cjystxRoRTO240e9P7cCy159/jk/lFCw7p9IBLojJ0bMehjTahjTVR14J31fFkh91g58jJeQWNI4w+425us6+rzIHHWoY+tecKKZl0Oux6Pe4urkV9qqHW6hieFkex08jOZV+02K/GoTgXBOt73n4wKnoUhzSKx/z555/P7NmzueGGGzhw4AAffPABe/fu7eEVnkCnMxETcyd6fSkbNr7U6FhJyVLc7hpiYy7o8Pz1Ow672KQ0VI/CVZlM/xFXtzl29NvvkrFrF2M2bmL8D0sY9+772EcOw+rsfRlyf9GCoz5RocrQcY2dcWgUjsNVuCqbesNotWFotRFYu9glt2jPUmpNe4g3Xo9G33baco9VIp1VqPQ9U9QJIMRsBiEo64aSog1xuVzk5uayZ88eqnqhZ1dZWRmrvP+SonWfQm3zPvzVDsXVNUgf1l1La5HpydM5HHwYoRHs2bOHPn368Omnn7J8+XKOHTvGvHnzWLWq7XKp3cXIEb/GZkuiqup9ahv8fwsK56PTRRMWNqHDc2s0DZJcVExixoWfce7Fy9DqfAuCPDnuyhgcTF1112VT7ii/aBuHfwRHJFVLDmPbU0rQGU29q5RqgF2rqso+9ApqjYXUs27xqb90qhGqni0ylThoEBQUcHjTJqIHDuzy89lsNvLy8liwYAEVFRXH24cNG8ZFF13UKwIlS0pK+PDDD5ESrpl1Bv0WvwZf3Q7XfAonrc/lVp5CdT7ekLqS/mH9GRQ7iP2O/cgsSVZWFkajkZtvvpn4+Hi+/vprfvzxR1JTU0noBUW8VCoV/fv/gSNH7mDd+seYMf0VnM4KSktXkJT4q2YjxH2em2CsxQZcdWqmndf5UgaGoGAKD/WeaqL1/KIFBx5Fhyw9Hdeza6KMqCMM2PaVtSA40iks/AYpZZekui7au5wa006SVHejMbQdoSulBJUJVc/GZJE0fjzG779n1c6daL/4Ao/bjcvlwuP20G/8OCL9aFQtLCzkzTffxO12Y7FYuOyyy7BYLOzevZt169ZhNBqZNWuW398ft9vN4cOHUavVJCcntzh/eXk533//Pfv370en0/GrX/2KxMREUD8F3z4Ia16GyQ80GmP35kDTa068kT8e/pG3d79NYlAiF6ZdyMSE5l1O/Y0QgkfGPcLVxVcTPiic86LPY+LoiceTG5533nkcOHCANWvWcPnll3fLmtoivd9MDh4YiUb7A0VFe3E4tyKlk5jYjqupAHSGYA5+048x519CaGTLAX++Yg4Nw1pZgcfjRqXqvtrtbfGLFhyaKBNCr8ZxpArz6JgOzSGEwDgwnJr1BXgcblS6xm+u2dwPl6sah6MIvb5j52iN7Kx/otaEkHrmbT7199TYESotKnMrGde6AY3BwPmTJvHVmrV8vXNno2PGPbt56C9/Qe2niGO3243b7Wby5MlMnjz5eCRzYmLi8XiDoKAgpkzxTyJmKSUHDhxg8eLFlJQo9q/4+HhGjRqFyWQiNzcXp9OJy+XC6XSSlZWFx+PhzDPPZMyYMSdqV4z5NeSsgqVPQvo5EDP4+Dls3rLERq8R91DFIR5Z+QgWvYW86jy+y/6Os5PP5v7R9xNrjkXfQWOvrwyOHMzjEx/nqfVPsaVgC89UPcPUECWAzmAwMGbMGFavXs0555zTK+I6AEaNepIdOy9g84a/ER3nwmRKIzhocNsDW0EIgd4chK3GP+ql4PBIPG431ooKgsJ7PpCynl+04BAqgSbc0GL0t68YBoZTszofe1YFxkGN31yz6YSB3N+Co3j/SmpM20lS3YHW6FuwknWbEr2q6+N/IdZeMs47j8RRoyjctg1dcDBanY4969ezqqyMwz+vpO85TWszd4T6ehBms7lR+gshBLNmzaKqqooVK1YwYsSIDhcccrvdZGZmsmPHDgoLCykrKyM8PJzLLrsMm83GunXrWLhwIQBqtRq9Xo9Go0Gr1RIbG8ucOXOaeiAJAXNfhAM/worn4Ip3jx+qs1UAYNWZqaot4Hc//w6TxsS88+YRogvh7V1v89aut/jxyI/oVDruH30/1w26rksLPF2cfjGjY0bz0IqHuHvp3QyPGs5b576FSqhITU1l9erVVFRU9ArBseXbNfy8aTVR6anExq6lohL6pj7gl/+PwRyErbb18tK+EhwZCUB1aUlAcPQmhEGDp65ziff0qRbQqLAfqmwqOBokOwwPn9Sp85xM9oFXUGuDSJl6u89jrJsOAtGYx7fvycpVVUXBH/+ELTOT0AsvIOT889Eldt4rKyQ+npAGJWVNiYms/ve/2b11q98Eh9FoRKvVNrJt1COE4Oyzz2bfvn2sWbOGc889t+kEreBwOFi2bBk7duygtraW4OBgEhMTmTBhAqNGjToe/DZ69Gjy8/OpqqoiPT3d96A4UziMvx1WvgBFeyF6kDJfbQ1aKZn59fm4pAu1UPPSWS8RaVRuNLcPv52L+l3Ed9nfsalwE89tfI5IYySzU1tOtOcPkkOSeWfWO5zx8RlsL97OmA/GAGB2mpnFLIqKiujTp0+Xnd/ldLFh/s846uxMOG8qhtCm6tuDG/eyYMMSwjXBJDjn4uafAMR2Uk1VjyEoCHutf7wFgyOUCI7q0mLi0ge00bv7+MULDk24AfuBzqX7FhoV2kgjruK6Jsd0ukg0Ggu1Vv8auEoy11Jt2kyCuBWd0fdCOY6jNpBVGAb6bqQsevllSt/49/Fo5uKX/0nxa6/T5913MY0a2e61t0ZoQgLRNhs5dt9zNrWE0+mksLCQxMRELBbL8WytJxMREcGwYcPYuHEjkyZNIiio+d2blJLq6mrq6uowm81IKfnoo48oKChg4MCBDBs2jP79+6NWN9VFCyFISEjomHH4jLth/RvKruPyt8Faxris1Xw84jIWxKYRagjl3D7nknRSTEeMOYabhtzErzJ+xQVfX8CCrAVdLjgATFoTSy5bwozPZpAQlEBeTR61mlpURhX79+9n7NiuKTRlLa/hw9ffJc+hZHPY/I8dzDxjGkNmjjnu/FBbXs3X380nWGXklntvRxekZfnPiuAwGv2T78tgDsJa5Z9o7+CIEzuO3kRAcIQbsFY5kE43Qttx45Mmyogzv+lThhACsznN71lyszNfRqU103fynT6P8bg9QARCV95oS+6urqbkX/9Cm5CI5eKLUDdQ11i3b6f0dSUPpWnCeLRxcdSuWYursJDD11xDvxXL0cb4We2lVqNxOjs9zcKFC9m+fTsXX3wxUspmb+j1TJkyhR07dvDTTz9xwQVNnzwzMzP56quvqKtr/HCg1Wq56qqrGDCgC58GTeEw7lZY9ZJi69j2IXhcDBh3DwNi2t45qlVqJsRN4Nvsb3F5XGhaiWD2F9GmaHbeoNiurE4r4z8aj9asbfL/8xd527P54psvqXDXMHvkdCISo/l20bd8ufY71m3ewJjho3A6HKzcsZ5aj43rZ1+J0bsbSdz8EBq7RSkO4Qf05iDKjvnHzdwQFIxGp6e6tHdVAvzFCw7Uyg1USuiMdlMTaaRudwnS5UGclPvKbOpHccmPnZi9MWUHN1Bl2kgCN6Ez+64vtu/NRmjN6JJP3JSly8Xha67FfkBxGS586in6vP8eFd98g9Dpqf7hBwBCzj+fhL8/B4CnzsaRm2+ibus2Dp55Fmi1RN17L9qYaCq//x7rmrXo+vSh7/xvOnR9dVIS1sn8RpWVlXxSWMHuUWexd+0W+peVIaVkyZIljBgxglWrVtGnTx+MRiMDBgwgMjKSM844gzVr1hw3lGu1WsrKyli/fj0bN24kMjKSadOmYTKZqKqqori4mLFjxxIf33ySS78y4S7Y8h58fQcg4OI3GhnL22Jc3Dg+zfyUBVkLuDj94q5bZzM4PcrnzVntJDrJP5Hk1rJqqkurcDocrF+2hl3FB9AKDVeeczEDJg0D4O5h/Vjz5U+s2beJ+RsXA2ARZq4770r6jj3hAh6VPB17tv/yQUmPB7ufbBxCCIIjIqgu612p1X/xgkPt9S5yV9pRRXXcJ14TYQQPuCrsaCMbB+GZzenkH/sUh6O01YIwvpK172VUOhN9J97drnH2g0pNZF3Kicw3NatWYT9wgKAZM7BnZuLMzeVwg/KVAKjVx4UGgMpooM+HH5I1Zy7OnBxwOil+8cUT/YXAnplJ/qO/J/7pp2gv4VJSqFbj8XjaHV+RWVpOfHAQHy7+kVX9huFWq1kxYCQ/9x/O5ZuWUbZ6NatXrwZg+/btAAwaNIgLL7yQadOmUVNTw88//8yhQ4cYOnQoS5cuxe12M3jwYObOndtztSWCouG+bXBoGZgiIKV9deSnJ09nTMwYHlv7GAaNoVtUVvXUOmvReDR47B4ivcbezvLl/z7lYI1SXVNIwbCI/sy87jyCwk+obTU6DVOvOoeJ9mnk7z2MSqsmrn8y6pM0CyqzFo+18zvceg5t2UjioM55ZzUkOCKSmoCqqnehT1We2G37y9F2RnBEKjcUd2ldM4JDybFfW5vVacFRlrWZKtM64uUN6ILaFzXsPFYJBKPvG0vNunXk3niTckCrxbpuHZ6GT0laLUGTJuGqqCD6tw80mUuoVPT9+ivqduwAIbBu3oKss2IYNIigM88kc/wEKr/+mqgH7kfbznxFaX36kFNURMH2HcSPHOHTmMziUq5et4u8IOX91FuSEEg2nZHB5T9tINsYRITdxgUXXnjcLTQ3N5fMzEz279/P//73P6699louueQSBg4cyBdffMHRo0dJTEzk8ssv7xWeQBhCIOPCDg3VqrS8OuNV7lp6F4+sfASBYFbqLD8vsHkKagswuZTvlr/+j2XWCgBmD5/GgPGDCY1vWSBp9FqSR/Rr8bjQqZBOj99irVRqNaGx/tuFmsMiyN+/p+2O3cgvXnBoIo1ooozYD1YQPLnjUa2aCEVYuEqb5pU5LjisBwkLa640u+8c2vsyKp2h3bsNZW21SGmm5LV/ULP4hxMHnE40iYmEXXstptGj8NTVoUtJQRPeeildlcGAeZxyPeaTDJ7RDz1E4VNPcWjueaSvWtmu9CbxQ4bCT0spyNzvs+C4e9Me8oIsDK4upVSjp8AYxN1BKkI8bopR0T8/hz4OGyNHjmTkSMWg379/f2bMmEFWVhaffvop//3vf7nmmmvIyMggPDycI0eOMHr06FZtI6cSJq2J12a8xp0/3skjKx9BrVIzs49/PNdaI6syy++CQyVU9DHGMv7iM9vu3AZCqwKJt8RC5wWH3hyEzU9eVQB6kwm71eq3+fzBL15wAGhjzTiPdU4nqQrSInQqXKVNjX96fRxqtbnTyQ7Ls7dSaVpNnOda9EHt27l4XG4cxWpch77GceAH0GgIPucc1CHBhJxzDsbRo/2auyr0qispfu01PBUVHPvjH7FccCHVS5eijY8n/PrrUBlbzqkVk9YXflpKaVFRq+dwezy8vy8LlZTsNAST6rCy9AKl5oHLI9GoBP9YuJQacwST1m7D7nY3O09aWho333wzH374IW+//TaXX3456enpxMb2TPbgrsSkNfH62a9z+5Lbefjnh1GfqT5eI7yrWJ23mli18r/0l+CIMIeSU5WPvdKK3tK5tCvCqw6Vbonwwx3RYPafOy6A3mTGUWftsuwTHSEgOKj/wHTuDRFCoAk3NrvjEEJgNqVh7aTgOLTnZYROT9q4e9o17uOFb7H9yGbGHt1HxoFjoFaT9v0iv8RhtIRKpyN91Uoyx42nasFCqhYsPH6s+MUXCTn/PGpXrkIY9JjGjiVk9hxUIcGYRo3CHBeH1umksoUkhHnlFVy7aAVHw6KoMZy4aVwTecIbTKMSOFxu3narGXE0h+S6GjzalqPlY2JiuOWWW/joo4/46KOPmDt3LmPGjPHDf6L3YdKa+NeMf3HHkju4f9n9/G3y3zg/rWvqelTaK1l5dCUXqi5EZ9C16OrcXsZNm8j+bz5m5RdLOfvmTq7d6yCD2wN0fndpCDJj82PWZ53RpKTkcTrQ6nouMWlDej6zW2/A7elQFcCT0UQYmt1xAJjMadRaO+6SW3l4JxWGVcSIi9FbfLMZ1NmsvPbp8zxV+g++Na/EUHIMgLjv53ep0KhHpdEQ/+yzoFaDWo1h8GCMXjVR1YKFuCsrcRUWUbVgIUfvuosj111PyWuvkb97N06tFksLu5L/W7GefXF90HjcRFZX8FeTh7s1Dm4bPqhRv09XrKEwJJQ7I8xUaLS421A5hYSEcNNNN9GvXz8WLlzIkiVL8Hi6p3pjd2PRW3h71tuMjR3LY2seY2PBxi45z6f7P8XhdqCv0NO3b1+/JZNMGzmAPsY4Nh7Zjq2mc2ocUe9Z6fZPbRh/q6p0RuXhyNGL1FUBwUGbVTp9Rh1pxFVmazZpotmcjt1egMvVsRw2Wbv+gfBo6TvmPp/HvP7tP3i9TklT8UzsX8ifrtgjVvz1Hhy21j+EUkrcVVW4Kyo6VWwpZObZDNq9i0G7d5H6xeekfPwR0Y8+QvDs2aSvWc2AXTtJ+u9/0KcrEfYlr77GK+99RrFLx9dDx/J1ztHjc7k8HnbnHWOtIYQMt43dsyayfe4Ubhk/ij9NGYe+gQtvdWU1L1XYSS0u5Lyzz8TodKDy4Tr0ej1XXXUVY8eOZfXq1Xz++ec4/RBT0hsxaAw8f+bzRJmiuPmHm7nlh1vILM/02/xHqo7wxvY3mBExg7qaOtLS0vw2N8CUqVOw42LHkk2dmkeoT6iq/IGtutqvBcr03geo3mTnCKiqAJVejbu888VSNOEGcEvcVXY0oY3dNk94Vh3EYmlftHXlkd2UG1YQ47kcY2jrwXZuj5vHP3uUBXWLcQlFp39b9I3MPfcyOPcyFlVfTf+F29gxYQxFKRbcfeLQolY8qqqq0ZVbMVbZCasF4VLGq0NDCZkzh6j77kXtzfvUGSJuuAFuOPE6aPJkzOPGUf7Z5xQ++SQ3LPgMgMtHT+CL7BJe332Q/hXFLLfEUGwOAb2RPwxMaNVo/ccFS8iPS+GjEBVqnQ4j4Grly3zgwBKysz/B5SrF46kCUcPYcTbKytbz1lvHuP76WzCb284+fKoRZgjjo7kf8dWBr3hvz3tc8+01PDLuES5Nv7TT+vR/bv0napWaGdoZbBFb6NevZc+mjtB3/EDMiw3szdzHOKZ2fKJGqqrOUZRziNw9O5l89Q1td/YRvVlR7/nTbtJZ2txxCCEMQogNQojtQojdQojHve0jhBDrhBDbhBCbhBDNugsJIUKFEJ8LIfYJIfYKIc7wtj8mhMjzjt8mhJjTYMyjQoiDQoj9Qoj2JQ/qACqDBo+tecNpezjuWVXSjGeV6YTgaC9ZO19GeDSkjW17t7Fiw2K+si3C5DYyXo7gs+mfcO/sB48fn/38x1Q/cz8Fo/tgrLKTvHQvUct3Ydl5GENxNcWaOnYkeyi7aDLRDz9M9CMPY54yhfJPPiFr9hwqvvgS2QXqG6HTEX7tNcS/8ALMPAeXSs19894hzVrFXq2Jz+LTKTaHEG+t4o8WNTPiWq7CvGLDFuYl9OWaolymjR0BgFmjxdrKQ+CBA6+gUq8AkYtK7UGjiUUlUoiLO0Bc/Pu8/vrvKSjofXUR/EG4IZxfD/01X1zwBaOiR/H42sd5ZOUjON0d32nlVOawOGcx1w64ln0795Genu53l2aVSkVKaALH6oo7VRrBn6qqzQu/Qq3RMHym/+JkTJZQAGrKmi/q1RP4suOwA9OllDVCCC2wSgixCHgCeFxKuch7038OOKuZ8S8D30spLxNC6ICGLhAvSSmfb9hZCJEBXAUMBuKBH4UQ/aWUnb+zt4B0eY5/eDpDfSyHq7QO+oU2OmY0JqJS6dqds6oqdx/l+mVEey7CGBrXat8v1n7C0/ueI9QTzI/X/ITe2Hyw2riLboeLlMSI9VtqIQRF1iIe/ep8BoYP5NZz/9UoNUXEr2+m4PEnOPaHP1Dx+efE/uXPGLqgAJNl7hwsc+cw/+nnmfLuWwy7+UaGTRvJZ7v2c6jGyp9mTULTyk7D5nbzcG4ZsULw+HkzjrebjQbq3O6WPVOEGocjlDmzG6s9duz4mALPY4wctZDdexZiNH6GxTLKb9fbm4g0RvLGzDf4z47/8K9t/yI+KJ7fjPKhVnYzfLD3AzQqDQOqBrCudh3jxnXODb0lomKi2V2eReXRUkKTOxhc6FVV0QnhU0+dN526rhWvwfYSkaDkICs9eoT08d1TY6Ut2txxSIX6PZLW+yO9P/VhmhYg/+SxQogQYCrwlncuh5Syoo1TXgh8IqW0SymzgYNA13zqGuKHD406RA8agausOc8qNSZTWrt3HFk7X0JINWljWv8Cf7bsAx7L/BsO4eJvA/7SotBoui5x/Ea6KHsRVpeVxyY+1iSfkWHgQPp8+AFxTz2F4/Bhsi+5lIKnnsLdRWUtz/7N3ZSFhJL96qu4XG5uGDmEx6eMa1VoALzx02pywiN5TOMgKPiEB09wiAWPWk1deUsJLXUI0TRL8rBhV9Mv7Y3jr7dsvZ6Skp86dE2nAiqh4vbhtzMtaRpfHvjyeLqQ9mB1Wpm3fx5TIqewcfVGMjIy/K6mqqe0sASVFGiDO+5tdHzH4er8TloIgUrtXwuA1mAgODKKsvyjbXfuJnwyjgsh1EKIbUARsERKuR64H/i7ECIXeB54tJmhfYFi4G0hxFYhxH+FEA0VxfcIIXYIIf4nhKgPg04Achv0OeptO3lNt3lVZJuKizuXAEwTacRd5UA6O/fBESqBJsyAq6R5zyqljKzvgqMqbz9lup+IknMxhbfsBfXCJ3/liSPPMqw2neVnL2bq5I5p937I+YFB4YNItTRfuUyoVIRecjFpi74j9MorKH//A9ZNnsHj9z2P2+miuriMn9/8mAVX38GyCdP4cdJM5t/7RyqK2r/Frjt0EEt1JVlxSVz+42ocPuify611vOZQMy7nIBfOPafRMWOooiap8dY4z8vbwubN/zlxbaIGj6d5d92+facx7awDTJm8HrO5Hzt23kF+/mftvqZTicv6X0aZrYyP9n7U7rHbi5VULv1r++N2uzn77LP9vTxAqYGyr+wQ6ZZkzGEdq6MCDQRHJx8erVWVHNqykdDYOL9X6wuPT6Qs3z+JE/2BT4JDSumWUo4AEoFxQoghwJ3AA1LKJOABvLuKk9AAo4DXpZQjgVrgEe+x14E0YARwDHjB296czqjJOyqlfFNKOUZKOSYqqmV9ty+oLToA3FWdT+WtjTG1GExoNqVhs+XhdvvmHZG1/R/KbmN005Qf9fy86Ufesc9jimss/7v5Q8ITOxa0lleTx86SnT6loVBbLMT95S/EfPgx/9/eecdHWd8P/P29vbL3JIGQMMIGWQKCo4gDrVpx1b1arVq1tto6qm2dtbQ/cVStlbqxti6WyFJkhRH2CCSQhOydy11uPL8/ngsk3OVGBgn0eb9eeeXunuf73PeT3D2f7/czK42R/GTZW2wZexZF06cR9+ffk7BrE41xydgtEQxc/m/y58ylcPu+kOZz8Pln5SioUcPYbonhp4u/DRip8tKytTQajPw2KwVx8s5EJ/+PaZWbdm3Pv5u6+mfZs+dzAARHgY5lydujUqnQ6WIZO+Z9oqKmsGfvrzlc+EqPRs/0J6alTGNm2kzmb5nPu7vepd4efBHAPTV7AHCUOBg4cCDRASoQhEp9WQ0bP13Np397D4dwMTR7aOBB/mgzUzu7979sC5cd/aOLuzcfH8iKo7jffN5CCsf1mJlWAbOR42L+7Tn0Cb7NScVAsWeHArAIWZEgSVK5RyG5gb+3G19Mx29wKj7MYD2JxlOjynawrtvX0qWF46qx4Wry7iooN3WSaLYeCnidxtID1OhWEOuegynG925DkiRe3P4SKc4EXrz2r+gNXberLiuUq4deMOCCAGe2m+OALH45417+M/fnHBszlYLzfkzDCwsYvXk9l37xHhctXYTj5dfQt9oouO0ObHXBRYU0lR3DmLeNluxBPHbX7Vxee5RV5lgeWLqy0zFHK6t51xTJ7ML9nDVtsvcJ4kTkTH1DMXq9XDSusOhffPfds2h1zYSFBb4BaTRmRo18g8SEyzh06M/s2/8kveh+6zOEEDw15SlSw1J5YfML3LTkJhpbgzNLHmk4QrQhmqbGph7Pvi/ZXciC1xbw9Y6V7K47xJDwDEbO7p4l+3g4bjeDPlxO2azXk/6NNqKSU3DYWmiu7R9VcoOJqooTQkR6HhuB84C9yDfztkIxs4ADJ4+VJKkMOCqEaGtWcC6w23Ot9p7ey4GdnsefA/OEEHohRCYwGNgYmlihoUsLQ5tioWltSUCN7qyzYd3ReaVKXZq8ZW4t9r5Jtg/JDUTBtr8gEGSNu7/Tc7Ye2sxhTTE3JM7DFGTrWF/YXXY+2vcRI+NGkhoWfGJgk92JW6gYc8MVXPru35j7t6eZeMlMdLoTJp8xF05n0dSbSW6sYMe9TwZ13eJPPkbrchN/880IIXhl7hwm15TykTaCzQd9/+2e/fYH3ELFo5NG+Tzu9oQWq3Radu96R5bbHo5Ol0eL7S1aW6PIzr4qqPmpVDqGDXuBAel3UFLyL3bsvBeXq/u71f5GlCGK/8z9Dy/MeIGDdQfZcGxD4EFAZUslCYYEnE4nxh6+if6w4jtcksRNl13Hrx76FfN+eROq7ibv9lBUlcsp+8jUfioUdJXoZPl72V/MVcH8xZOAlUKIfGATso/jS+B24CUhxHbgj8AdAEKIZCHE1+3G3wu85xk/2nMuwPNCiB2e12cim7uQJGkX8DGyglkC/Lw3I6pA9k2YRsfjrGrB3dy5M1Byuil7dhM17+3p1KylTbWAClqPeJfLMBoHIIQmoOJoOlZAtW45sc45mGI770p20FMxc/LwbsSwA+/veZ+SphLuHXNvSOMsetkJWN/i34E6fM55fJE5BfOmrzm2Mi/gda37ZLNW/Ew5KkqlVjN/1mQ0LhdP53snqB04XMR/YhK54thhBg/3vWuQXPKXWqXVUVf3DS0tsRiNcoE/l8vA9GlfkZgwOuDc2hBCRVbWIwwe/FsqK5eybduNOBzd6yTZH1EJFZnhvn1enWF32TFJ8i6+p2srVdRXkaCLImP0YEyW7tWoaqNtx0E3FUdbVrzb2b1W1L44oTj6h4M8mKiqfEmSxkiSNFKSpFxJkn7vef07SZLGSZI0SpKkiZIk5XleL5UkaU678ds8voiRkiRdJklSref1GyRJGuF5/VJJko61G/MHSZIGSZKUI0nS4p4X2xttovwhdJR1Xuyw9Kkfjj/2FTkFoNKp0SaYaT3qva1XqbQYjRkBa1YVbHsZgWCQn90GQHFrKUISREV23YZca6vljfw3mJ46nUlJk0IamxplwqLXsKPEv/37pkty+GzIhTTrTJT86Xm/5wI4jh6lVadF3y7ZMD0qiovstWyITGT94cIO5z+3djNql5uHZ3Xe092lPcRZExex/vCj6PRHMRgmMnnS79BpryN3+N8xm7vWxTA97WZyh/+VhsbtbM67Cqu1qEvX6c+4POs2lQhuZa9X63HbZLPPihUremweNQXlVDnqSUrq2cZZJ/I4umeqMlhka4OtqecjDS3RMWj1BmpKjwY++RSgZI57UEfK4XyuBm/fhOSWKHn0u+PPzRMT0Wd0nsykSwvDmi8nJQlVxxWXHFnVuaO4sXQ/VdplxDovxBw3wO+cCxsKSXLFEhnR9R4fG8o20ORo4o6Rd4Q8Vq0SjEmPZNNh/yttm91Jtc7I+vRpnHdwCRV5+4gf56fVamUVzqhIr5cfnzqBxZv281T+ARZnZgCwc2s+X6dkckN1Campndu6Y831VOhbALmdaWbmpRgMYUyb9vtAYgYkIeEi9PoE8nfcxea8Kxk18vUzKtejzXx738r7mJI8BbPWTElTCXannfTwdAZFDuK2Ebdh1soBk3q1nrrWOoAu+TiaqxtorGwgcUhHs+n65d/hRmLiBZ0vELrE8czx7u04DGFydkJLL4SoCyGISU2j+uiRHr92V1BqVXmQWuXVhq9EwIZlhccfC6OGiIsG+r2WLj0MyebyGZZrNmdhtRbhdvs2dR3Y9hxCUjN4/EMB59zsasYszD1iDjCou9bZbtLAGPaVN1LT7K1wAXbuq+aKZ1bRKiB87mVICIr/7j/EU9fUDHHeyVzJ0dFc2lLF1vA41hTKX6Dn8g+gd7Ty4Pn+zXV6IUdZRRnvQa2+ksyMni0lHhk5nvHjPkGjCWPL1usprzglG+VTQowxBoPaQLQhmnp7PQV1BYRpw0g0J1JQV8DbO9/mhsU3UNpUyuayzaw8spJG5JvntGnTQn6/t199k9c+fJPFr/+bqiNyaf1Wq538sr1khqUQl9qzPe7bFnfd9XFotFo0Wh12a8+0jT2ZmLQBVB3tHztaZcfhQWg9OvSkz469sJ7GVSfsirE3D0el8x+jfdxBfrQRbXxHO6zsIHdjtRZisXRcddcc2kytYRWJrnmYYjsPDQV5FXjUVUKWYRDv7HyH6anTGRjpX6H5IsUsp8iUNpWSE+1nF9AJY9IiAdhd2sDZg71v9o9/ks8uh53xYSau/ulUdn06Gs0PS3HaHkVj8E7actrt6Fsd2DtpMfrY1Il8uWk/v99Zy4s2O8vTB3F75VHio/yb2STPP3ZI5kWYErNDlDI4TKZMxo9bRP6OO9m58x5sWb8mPe22ftNDoaskmhNZf+161J3kJqwrXcdDqx5i9qezkZBItaRyV9pdbDy0kbCw0PIrmuoaqXbK/sENx/LZ8HY+5w+dhgBsOJg8JTRzalBo2oocdj8BUKPT4Wz1vYjqLnHpmexa9Q1NtTVYono2xDlUlB2Hh7bkP3FShIZ1+4nkwoiLBqJPDycQmjgTQq/26eforGaV2+1m/+6nUTnMZE160GvcySx8fwHHtFVUamp4Ke8l5v53Lg+sfIDVR1cHHNuetiiq4qauOd0sBnntUWP1/WUparYx1mhk0WMzMeg1RM67Gp29noI3PvV5fnNxMQJQd6I4EuPiuNxayU5zFPfsK8bcYuW+c3yE356EW8jzU6t6PlSyPTpdNGNG/4v4+DkcPPgs+/Y/gdvd887SU01nSgNgSvIUFs5ZyMy0mdyaK9e8StLKQZOh9t84uucwAPOmX8atV95Isj6W5XvWsmzPWmK1EWRN6rle3m2IHjJV1VeU43K5cLb2ToRdYpa84Ck72HMVjLuKojja6CRrVG3RHX9s2xdcDLVQCXRpYT4Vh8mUCai8FMfRje/RbNpJuulO9GH+VxP1hyv5s+MNAEqlchJMCUxOmszq4tXc8+097K3ZG9Q8ASL1kZi1Zo40hG47bWl18ehnOwjTa5iQ4d3/3OVy0+h2kxBxwgyWef3F2MISsS78O45m7wCDZs9WXOenT/mdkycCcDA8isuqSomND1yjyO2Uk7M05siA53YXtVpP7vD5DEi/k5KS98jfcSdOZ++YL/oLgyIHMX/WfO4fdz8mrYlGj50/FMXhdrnJ+34jQhKkDc8kLTeTebdfj/DkBF966dwe6+fRnp4wVW1b9jVv3nsrKrWKMbN7pylWfOZAVGo1xw4E//3uLRTF4UHo5D+F/UjHm7028USFFMkefFSwLi0Mx7FmJEfHMWq1AaMxrUOxQ3tjDYfrXsbYkkXmlDsDXnv1lmW4hLxD0qq0LDhvAW9c8AbLrlyGUWNk4e6FQc9TCEF6WDpHm0KP1nh+eR47Sxp4+erRJEV4r+Rb7E5aBUQZT8S1qzRqIu59EENjGbsefcl7jKckiCGp84KO2cmJGFuaEZKbuyYGV6Le6bRCK6hMPdOBLhByuO6vGJLzDDU1a8nbMg+bveyUvHd/oKmpCb1ejzbInAa3y82SNz7jYFMxUweNw5wgB5+Ex0Zy1423c+PF15I+InRTbFBouh+Ou+KtBQBc+dgzxGf0zjy1Oj3xmYMo2benV64fCori8OCokFek2riON8D2N35tUvD9GHRpYeCWaC3xnQjYfsex7/vf49I2kJPz+4AF0iRJ4rGmZwFINCbw4cUfkh0lb2FjjbFkRWZR3RJabai0sDSONoSmOHZV7eLDArn433vbfZvHjq/kTtrNDfzpxTQPmYp22QeUr8vvcKz1mByVbQrQoXDkvu2M27GRwTnBNQdyuayonOKU+xtSUq5h1Mi/09JSxObNV9DY1PerxVNBU1NTSP6Nr15fxMbyHQyLHcS5N1zU4VhCZjKZ43vHLwXtdxxd93EMP0eux9VbSqONlCHDKSvYj7OPm4spisNDW1SVLr3jh73x+xPVTtQRwVfgbAvvdTd5/4PNpiys1sO43U6O5S+mUvcFcY5LicmaGPC6rmYHw63yzfKTSxeRaO4Y7mjUGLE6Q+sUlh6eTmlTKc4gbfG1tlpuX3Y7KUlHiY0vYOV2M/f8+xMOHCtng8dGDbB1t5xhnx7rrXCH/PUZ3CoNRc//X4fX3c2ySUdt6fymI4Tg9gNbOG/t50EnW7ndLQhH33zcY2JmMG7sRwDk5V1NdfWaPpnHqaS5uRmTKbgEvZ3L88ir2M3wmEFccfe1pz6YoAcyx5MHyy0GGqq6V3A1EClDhuFyOCg/1Le9YRTF4UGbIH/I24fQuq0OHO38FOownde4zjheaddHOQSzOQtJclB7bD37Sh9F35LG0BnB5RM4y6y8WPQgGyesItIQ6XU8zhRHhbUi6HkCpIel45ScHGs+FvhkYEv5FhodjTw3/VlW3XMniXEVfLnRxFPPbmPz/MM8++gaXn9jG2+9s4NhrWp+dJZXcWMs6Ym4J52Ped86qnef8K8Y0uVM+aYC/1+MuAGZuNQqKvO3BzVnFzZUzr4LIgwLG8r4cYswGlPZnn/bGV9d1+12o9EE/nu73W6WfL+caHUYc2/7id+ujr2FEAJUolumqoaqSoRKhSW66zlVwZCSMwyAkr27evV9AqEoDg9uj/9C6NS4rQ6cVS04q084b3WZ4ZjPCj6ZyVUnj9VEeu9S2mpW7dz9C9zqFnKH/wVtkLWmVCb5y9hZ74B4UzwV1oqQqmimhcmhv8E6yFcVr8KsNZMbm4tFp2f1fddz2SQbOmMhAIYaB84tNYxv1XCRVUdlvu+ggoz7b0cluTg8/+3jr1ly5JBg6wGv0mcdSB45GoDCtcFFkbmEHZWr52sIhYLBkMS4sR/9T1TX1ev1NDR4l905mZqSSpqwMTZ7FDpj13tqdBehFt0yVQmVCqSeL7FyMqbwCKKTUyneszPwyb2Iojg8OCtl847QqTn23CbKXtxM07p2RXlDXI04yq2gOtFOtj0mk2xqcqrryTDeT2TG6KCvq/KEv7qtvk00CaYEHG4HNbbgq2gOjhqMRmhYf2x9wHNtThvLi5ZzXvp56NXyF12v0fKXy65g0nB5tXXRoyMZeWsOxnHy881fFbLqfW/bfsSIbGyDxqL94WtaamUTVWTuCCSgpaDA7zxSZp5LuNPNlnUrcToCx827RStqqe9uTG1oNJYO1XX373/qjKyum5mZSVVVFfX1/svRlB2Uw8ATU/x3t+x11Kpu7TjCYmKRJPcpqV6bNnwEJXt343b13edGURwenFU2hFZF69FGJLsLbaoF69YTJh/D0NASbhzlVjQxxhOJhe3QaMwYrBlENEwjY0popT7anPiaON/5CG2O8o1lwRcUjtBHMCVlCosPL8Yt+V915ZXn0exo5kcZ3s2iomJlv0R9QznTJqRwy+2j+PHDcumNXWtKqTziHZ6ccMfN6Fob2fe3DwDQh0fQYjHjOuDfVKUyGJg443ysSGz5+6sBZXSrHajpe8UBJ6rrpqffTnHJQnbuvO+Mq67b1vGvIMACoOyoHGmWONR/wmtvIzTd23GEx8gh4fWV5T01pU5JHZpLa4uVisLA7Rl6C0VxeHDV2pAcbuq/PozKoiXu9pFEXZl9PAtcn9l5bSpfOCusaOI7dw4OrvgzqUfuDTou3W130rT+GHWfF4A4kZ1+MuMSxpFgSuDrQ1/7PN4ZczLnUG4tZ0v5Fr/nrT+2Hq1Ky7iEcV7HsrPlL//+fScitJIGRTL+ogwAPv7jJhpOKsOSdOm52KNScH71CU5PBJtLq0HlDryayr3r50Q53GxctZzDX35Ba31dp+e6tS5UomeqqfYEQqgYnPVrBmc9RkXlYrZtvxmns3fa8PYF8fHxhIWFsX+/d7Ja5eEyvv3X17z6zHy+O7QJAL25ayVvegqhFkjdaOQUnzkIlVpNwebgSs93h9RhIwA4untHr79XZyiKw0ObqdmQHUXcbSNQ6dWYxyccT+JzlAafwCVJEq46O5qozr8MujgLrkpb0O0qG5YVUfefg7hqbOgyIjote6ISKs4bcB4/HPsBm9N3BV9fzEybiUFtYPFh/zWW1h9bz+j40Zi03jfh4RnZ2DTNlBd2NE9MvGQgsWmyD+dYQV2HY0IILFdeg7n+CAc/lHt565qtuKO8EwpPRqXTMfnCudgF/Hvh67z3s1t9+gwkScKtc6NW9x/F0UZ6+i0MH/Yy9fVbyNsyD7u991espwIhBLm5uezfv58NGzZQXV2Ntb6JN/+0gFf++RprDm7EJbkZHT+EszPHozUEH3jSK2hU0I0dhykikqwJk9m9dmWv+60sUdFEJaVQ3IeKQ6lV5SH66hzcLU4vZ3byk5Mp/+tW6j4vQGXRYhoZuE2t1OJEcriPh+T6QhNvQnK4ZQUT7X+1ZS9qoOn7UtQROqKvG+pV/+pkpqdM570977G2ZC3nDzg/4HwBTFoT4xLHsaOq8w9jdUs1e2v28osxv/B5XKPWYI+uR13mLffws1NY/cE+Gqu9TTKZd85j9zsLaP7XQhyXTUTf6sCZEFwhu6G334k5K4stHyykoL6KolXfkuHp49GG225HMoDG3v8UB0Bi4qXodDHk77ibzZuvZPTodzCbg8tP6c9MmDCBvLw8Fi/uuBiZkjaGMTPPIm5gH/s12iFUottFDtOGj2T/+u/4/qN/MfisySQMzOqh2fl4r2Ej2LtuDW63q8f7mweDsuPwoNKrfUZAqQwaEn85Dm2KhbrPC3BbAyfeOOvkm6O/vI+2m3+bU74zmvPKqXxVDjmNvnYo+vTw4w7yzpiYNJEUSwoLdy8M6LNoT2Z4JoUNhbg6MROtOCL3VpiW2nnFU2OsGlNjJDZbRwURHisrx8Zq74rBaosZ9ayLCS/azKEPvgRAP8B/Sfn2pM88lwsefwaNy83m997xWvG5WhpA3ft1qrpDdPRUxo59H5fbTt6Wq6mv39rXU+o20dHR3H333cyePfu4zwPg/Fsu7VdKA4AeCIYaOGY8hrBwNnz2ER/87qFe7daXOkz2c1QWFfbae/hDURxBIDQqoq4YjNvqoO6rwwHPd3kUhy9F1Eabc9tR4X0jPX6dejv1SwsBiLlpOPoBgQssglyQ7pbcW9hasZWn1z9Ni7Pz92jPmPgxtDhb2FS+yefxpYVLGRgxkJyozqvo2iugRd+IRttxFRSdLCcBFu6oxu3DJJD54B1IKjXuPz8NQOyk0KqgmlJTGRgTT1FjLR9cfxWtjSdCQR1WuV+IWh185n9fEB6W6ynNHs6WrddTVdV5j/XThaioKCZNmsR11113/LVPf/cPrPm9myjXF4THxXPnq//kppcWoNJoWfPe24EHdZG49AwAavuoI6CiOIJEl2whbHoa1rxybAf8Ny5y1Xt2HH4Uh9qiQ2XSdLrjkBxuyudvwd3QSuwtuRiHhBbVdVX2VdySewuL9i/i2q+upbE1sOP1rES5EdKBWu8cCofbQX5lPlOSp/iPVRfgNNvQnFQ6xRJlICrJhLWhlX/+Zh3uk3w7xvQUbNMuP/48ccbMgPM9mR+99DdGDxzCMaeNd26ax5KH7uPIt9/gtHkUh6Z/Kw4Ak2kA48d9jNmcRf6OOyktXdTXU+oRhBA8/NDDANhEK40r+0cnu55Go9USk5rOpB9fTcHmDRzeFrhVcleISJBzyurKgkva7WkUxREC4eemo4k1ypFNfnDW2UEjUJn9J5xp4k3Hw2tPxn6kAbfViW5AOIbswI7ikxFC8MC4B1hw7gIO1x/mhU0vBBwToY/ArDVT3Oi9ijlYexCby8bIuJF+r6EKc2FoDMflY1cx77GzSMgMx9rQypLXvX0pOb+WCzxWWQzsXBV8OHEbOksYs/74AmMGDEalVrOn6CCfvP4X1s6X29xrNKH1hugrdLpYxo5535Mo+AiFhQvOiERBs8XME088wSWz5uA41uyz0VlfIbW6fTZx6yrjLpqLOSqanSuX99g126PVG7BERVNX3jeFMxXFEQJCq8IwLAZnrf+Ye1edHXWE3qtt7Mlo4004O1Ec1k3yByLqqu4Vd5uWOo2rsq/iy0NfBkwKFEKQaEqk3Ood2VPaLCdDDgj373tIHGJB7zCxJd+7gqdKo+LyB8dgsGg5vL2Kj57pqBwsmSnELl1JXs4Ilr3+HAVbQs+OFUIw6/mXue3jL7j9xQUMMFgo9nSjU2tPTWXcnkCjMR9PFCw49BL7DzyFFIK/qr8ihMA8Rg58aN4aWmmc3sJtc8pBKgk9Fzyh1mjJGDmG/eu/o/JIYdDjJLc76EVCZGIydeWlgU/sBRTFESqSBG4Jd2vneQauOjuaIAoiauJMuK1OXE3emc8uqxNtqgVtbPcdulfnXI3D7eDzg58HPFetUtPs8A49bqu4G2PwX4tn1pSzcAkXeZt891VXa9Tc8IfJhEXrqSpuIm9px1aYcQMSueK3zyBUBj5/8WnqK+oCzrkzLAMGMOuXv0FrlLPsHeL06olxIlHwNoqLF7Jr9y/PCOWhidQj9GokW/9ocGXbKy+o9ANCy9UKRO4556MzGnn34Xt4/a6f8uX856mv6HyHsGftShbccT1/vfFK3vjZzdQE8F9EJCT23x2HEMIghNgohNguhNglhHjK8/poIcR6IcQ2IcRmIcRZnYyPFEIsEkLsFULsEUJM9rz+gue1fCHEZ0KISM/rGUKIFs91twkhXutBebuNcXgMuCUq5m+htdS7ZHrz1gpajzZ06OPRGdp4WSk4fTjI3Q2tqAOYuoIlKyqLMfFjWHRgkd/VzO7q3eyv3c/EJO8qvQ2tsrM5Qu//y5Uam0xjTBmN+zt/H51ew6TL5HDT9Z8V0HJSv/K0oelccOfDuF3NfPrsK37fLxDRo0aRlS03hap29F2mbVeREwV/w4D0Oygv/8KrAdjpitCITuutnWqaN5ahjtSjywgu+CRYUoflctvf3mLmTXeSOmwEh7Zs4v3fPkRR/jaK9+6iuviEn6fySCGLX3mZqIQkBo09i8bqSg5sWOf3+paoGJrr/Ptbe4tgdhx2YJYkSaOA0cBsIcQk4HngKUmSRgOPe577Yj6wRJKkIcAooM2GsRzIlSRpJLAf+E27MQWSJI32/NwVoky9ij4jgthbcnE1tNK4qqODr2nDMWo/3oc+M4LwH2UEvFZbZrnjJAe5q96Oo6zZr3M9VK7KvoqihiK/pUhe2fYK4bpw5uXM8zrWVnJdo5Kd3pVH19FYuoWtW95gy4Kx7F735+Pnxg4xYGqK4kBhkdd12sg+K5GMEfLu5eAmb5PF8OmjMUaMpbZkfbdLK6ROvAEAffjQbl2nLzF7+tOrVH2cKNdDqKMMOCt71schuSRai0PLvm8tbcJ+qB7LlOSApuWuYAwLZ+yFl3DRLx7muj+8hN5kZtEffstHTzzCPx/++XEfyNbFn6PWafnxb57i4vsfITErm73f+y/gqdZqQJJwB1FloacJqDgkmbaltdbzI3l+2lR0BOBlbBNChAPTgbc812qVJKnO83iZJElte9X1gP/OPf0IQ3YUhqHRtBad+JA2ri2m7rODGLKjiL1pOCp94KQcdYQeoVV5+TlcDfIKXNMDZqo2zh9wPuG6cD7Z77ucd35lPmuK13Bz7s1YdN6+gLZ8ELVQs3frP4h660LC3pjJmM8fZmxFAYOXPcW+HXK9qWlT5a58a9b5L1/S4Kk+bInyVpDlhxtwMxGtwcyKt1/rlnNYo5EVtOQ+fS2zbeVINJrTx0/jD228CUcPKg5Xs4PKv+dT8X/bQgr1bfq+FKFTYR4fXMJpd4hJTee6P77MzJvu5OL7HyE9dxRLX5vPdx8uZM/3q8mZPA2Dp9Vu9sSpVB0twtrQeZFItUa2SLiC7EnTkwT1TRJCqIUQ24AKYLkkSRuA+4EXhBBHgRfpuGNoYyBQCfxDCLFVCPGmEMKXDecWoH16aabn/NVCCJ/ZZkKIOzwmss2Vlac+Jlw3IBxXvR1nnZ2Gb4qo/+owxhGxxNwwDKENLpNTqITPyCpHmWyLD7U+lj8MGgNzs+ayomgFVS1VXscXbFtAlD6Ka4dc63O80+1ELdTs2vY2kV8/gkMI1mRNZWf2LGruXEmtVgfLHgPAZZVv8u4AVV+HTJZDCksPen858lcWozeHMf26myjdt5vVC98KSd72OFzydl4letYUcSpxHVccp0dkWCC0KRbcja3Hk2W7iuR007i6mLIXNtPqKaJZv6QwKDOYq6kV67YKTGMTUJlOTcl9vcnE2AsvIWfyNC5/5HGGTJ3Bhs8+wmm3M2LmBcfPS8ySg2LKCrxrfbWh8vQuCbaZWU8SlOKQJMnlMUmlAmcJIXKBu4EHJElKAx7As6s4CQ0wFnhVkqQxQDPw6/YnCCEeA5zAe56XjgHpnvN/Cbzv2bmcPKc3JEkaL0nS+Li4wGVAepq2ZLya9/fQ8M0RTOMSiJ43BOGjcZM/tHHGDlt2yeGiflkhmnhjUH6SUJiYOFFu2NTUMfbb6rCyrnQdV2Zf6bMGFUCLs4URLVZy//tLEh12ii3RTL/+a3Kv/YzopLEcTB9PVmM1LkcLy96Vo6GmT/YuhNie3OkpIGDn6mJamk9k5DfX2SnIq2Do1CRGnSd/mfK++g/7fljbJbmbPX1GwiIyujS+P+B0NiGEDpWqf1T47S5t35/Ww/7LrvvDfqieY89ton7xYfQZ4cTfM1o2I9fYaFofOL/Btr8WXBLmCcH32elJ1Botc+55kPPvuJfxl/yY5JwTptSETNkHWHGoY+h/6f69bFv2Nav/9TZOu6x02/tKThUh1aqSJKlOCLEKmA3cCNznOfQJ8KaPIcVAsWeHArCIdopDCHEjcDFwruSxRUiSZEf2qyBJUp4QogDIBjaHMtfeRptklsuwH2nEPDmJyEsGdclGqok3Yd1WidvuQqVX0/h9Ke5GBzHXhK6EAtGWuPfC5heINcYe71He0NqAhMTo+NGdjq1qqWKgp8/x1ql3kTLimg7H9VGZqFnHe3+/F0P9tYiJleQMnOV3Plq9hlHnprH9m6P846G1TPnxIEafP4Cda0pwSxIjZqQiVCquefpFPvjdQyx59S8YwyJIz/WfS3Iy1uYSUEFk3OCQxvUnnK7GM8ZMBaBNtqAO19GcV45pTHzI4912JzUf70PoVMTelot+UCRCCCRJQj84koZvijAOi/FbB671aCNCp0ab1HeJoUKlYuS53i0KdEYT5sgo6itP+P8ObPqBL1760/HIOq1BNmUf3ZVPcvaQUzNhDwEVhxAiDnB4lIYROA94DtmnMQNYBcwCvNKNJUkqE0IcFULkSJK0DzgX2O257mzgEWCGJEnHbTWe96uRJMklhBgIDAb6XTiMUKsIOzddru46PaXLnb861KxSCRqWFWEYGo2uB81UbRg18gdta8VWks3JNLQ20OSQ3VdpYWk+o6naqGqpYrrDgQMYNv1R9CdHVxnlJMXY/enUqmzcdPWlQc3p7CsHY7Ro2fjFYb7/dwHpubHsWltCxohYIjxlWZKzh3DX6wv55OnH+OzZJ7nyd38gJSd4R7fNVoZLrSI8pp/VRwoBp/PMUhxCJTBPSaZhSSGOsuaQdtfO6haq39+Lq95O3F2jOpTiEUIQdVkW5f+3jap/7CT+7lGdmqGc1TY0ccZecYr3BNHJqexcuYziPTtIzx3F7tXfkjhoMBfe+yDWujo+fOJXANiavaM7e5tglrRJwEohRD6wCdnH8SVwO/CSEGI78EfgDgAhRLIQon0ziHuB9zzjR3vOBfg/IAxYflLY7XQg33PdRcBdkiT1flutLhB+ThphM1K71S7yRGRVC7WfHgAVRF+V3SstKIWnktvQ6KEsvXIp665Zx5IrlvDKua/w/pz3j3f080VVSxVpDidaYN+Sh72Ot1pkc2GNMw2Dqh6jXk/hjirylhSyY1UxTj95L+NmZzDrhiEgwX9e3kJLo4ORszrGSpgjo/jJE3/CHB3N4ldeotUWvGPV0dqI26XtkyqiPYUkuRDizCpm3bbTsB8K3lwluSWq3t2Ns8ZGzPVDfdZv08QYib1hGM4aG7V+qjy46mw9GrnY08z46W1kTzobg9nCjm+XkTp8BJc98jhRicmkDBnG5b9+gmHTZjLtmhtP+dwCfhIlScoHxvh4/TvAy4gtSVIpMKfd823AeB/n+aw5LEnSp8CngeZ1pqCJMYBK4ChpwlHShD4rstccdaPiRnFZ1mXcPuJ2QF6dpVhSSLGk+B3nltyUNZexbvw1nLfmbUZu/YiSsTeTkjb5+DkTxv+cXdowor+w01CWyFcLdnBkV/Xx44U7qpnzsxGo1b7XKjmTkvjmnT20NDiISjKTmuNdZsUUHsHsu+/no6d+w4q3XmX2zx4ITsG69ag0sgOxsXEXzc0HiYgYg9GYHnhsP0Gl0uN2n1ldAtXhOoRRg6M8+MRMt9WBs9xKxIUZGIfHdnqefmAEYeek0bjiCC0j4zAO805cddW3oh8U2ZWpnxISMgdxyQOyZV+SJK/P+sAxExg4ZkJfTE3JHO9rhFqFJsZwvG6PtgfLHpyMVq3l6alPkx4e2g2z0lqJzWUjOmoQ++e+DEDRdx3TdjRqLcNG30JxRSpOSc+RXdVMuSKL2/8ynRnX5nBkVzVfv5JPTRANsUbO7HwXlzo0l8lXzGP3mm/55s1XcNgCN6vSaMNRaV0UFb3Oxk1z2bX7l6z7YSY/rD+PI0feOi3qQJ2JikMIgTbBhKPMf2uB9rR9T4Lx/4Wfk4Y2yUz1+3uxbqvo8H92211Idpff1gf9id6wQHQHRXH0AzRxJlqPyJnZ6vD+l+AVa4xleMxwXs9/nT+WrwFgZMF3Ps+1uWTFlz0xgTHnp6MzaMidnsK0q7MpO1TPJ89tptpHxn17sif4j6mffMU1jLv4cvK/WcI/HrybAxvW+b35azQmhICDBc8TH38h48f/m+zBj6PTxXHg4B85fHi+3/frD5yJigNAm2jGUdYclPJ2VFipemc3qjAdhqH+S9+AXFsu9tZctIkmaj7cR91nB3HW2uTaVA2Be+YodM6ZZTQ9TdHGm7Dtls06moT+V/pbrVLzzux3eHf3u6wolDNdLc5WOLQKMqYDEggVYsWTjDeXU6afxox50ztcY+TMVDJHxfLRMxvJW1zEBbcO73C82RPPbwrXoTX490UIlYpzbriVrAmTWPHWq3z+5z+SNDiHadfeRJqnH3N7nDYnGCEm+hxyh/8FIdREhI8iNfUG9u59jMOFf0OotGQM+Fmfruxs9jIqK5dRXLyQ8PBRDMn5A2qP30mrCcfpbMbtdqBSnZqcg1OBNtGEZHfhqm/ttH9NW65G46qjCJ2K+LtHBeya2YbaoiP+Z6NpWFZE46qjNG8sk5fLnjSP/rhQOx1QFEc/QJt0wjylS+ufCV4GjYE7Rt7BHSPvoGXsZgzvXYl4dy4INejDIDYbijcy8ZybYc5PQO390QqLNjBoXDz7N5ThsLvQtsuu37mmBAT8+OFxQd+8U4cM54Zn57Nr9QrWLXqfj5/6DZmjxzHrlruJ9PQraGlq5MA3NuKy5zDrfllptCGEiiFDnsHttnPo0J9patxDTs6T6HSd2857CoejgcbGHTicDdTW/kBt7Q9YrXLwoNGYQVnZZ7S0HCEn5/eEWYZgMKQCbmy2Ukym4Lsj9ne0noWSo6wZdYSOlh1VCLUKXaoFZ7WNlt3VWLeU47Y6MY6MJeKCjKCVRhtCJYiYnYF+cCTOCivOWjtNa+QCgori6BqK4ugHtG27zRMSe6ywYW9iTB0Pv9gKG98EhxXqiqB8N8x8DKY/DH5u/NnjE9i9tpTCHVUM9pR5cDncXiG4waJSqxkx6wKGnD2DbUu/Yv2nH/DPh3/ORb/4Fcf272Hrki9xOlqZ86OHOyiNNoRQM2zYi5gtORw69DI1tesYnPUoSUlXBKXAHI46qqpXUVe7gejos0lIuMjv+TU131NW/gWVlUuOlxFRq01ERp5FSvI8oqKmYLEMobz8C/btf4qNGy8hJWUe8XGzAbDZis8wxSEvmqrf2SU7s0+q/4ZKYBweg3liIoas0PvStMcwKBIGRSI53Yri6CaK4ugHqHRqkp+cjNCeRi4nYxTM8A7LDUTy4EgsUXp2rComa1w8QggO5pXLIbjndL1cmVanZ8IlP2bIlOn898U/8N8X5Ba0OZOnMeHSK0gY6DOID5B3HhkD7iQu9lz27H2MPXsf4VjZv8nJeQqL2XfSoNvtpLhkIQUFL+B22xFCS+mxj6mqWsGQIc+gVnsHOVRWLmfHzp8fVxSpKdej0YQRFpbrZX5KTLyUmJjpHDo8n+Lid6mv3wZAS8uZ1TmvfQRh46qjGIbFYJmchLOyBXWMAV2KBbWlZ2/u7R3rKr1yC+wKyl+tn6Ay/G/8K4RKMOaCAaz9aD/Fe2pJGxZN/spiohJNpA7t3ooSICwmlsse/i1v338nSYNzuOi+XwVt+jKbsxg39gNKSz/mYMFzbNgwh+Tkq8jM/AV22zEqKpdSXb0atdqM09mA1VpATMw5ZGb+gjDLMAqLXuPw4fk0Ne9j5IhXO4T7VlWvYueu+wmzDGfMmIVBJfNptZHkZD+ByTSQ/fufBMDeeub16k75w1RKHvsegMiLB8qmqMHd/ywEQgRRiFTBN+J0CEUMxPjx46XNm/tVRRIFPzgdLj54agMIwczrh/Dfl7cyfV42I7qx4zgZW1MTWoMBtaZrCrm1tYbCwlcoLnkPSZJLrQihJSpyIi5XM26plYwBPyMu7kcdFFN19Wp27rofl6sFiyUHkykTR2stNbXfYTINZOzYD9B3wYdSV7eZ0tKPSE29gfDw0EqunA607KzC1dCKZUryKXk/R4UVqdWFLrV/+hRPFUKIPEmSvPLsAo5TFIdCX1C8r5b/vrwVAK1BzU3PTkXXD3ddVmsRFRVfoTckExszC602cIXdlpajlJS8T2PjLqwtRbjddtLTbyUt9adnTJFChTODriqO/vdNVfifIDUnimFTk9j9/TEGj0/ol0oDwGQaQEbGz0IaYzSmkZX1SC/NSEGh7+mf31aF/wmmXjkYjU7N2B+dOVFCCgr/CyiKQ6HP0Bk1TLs6u6+noaCgECKnUfyngoKCgkJ/QFEcCgoKCgohoSgOBQUFBYWQUBSHgoKCgkJIKIpDQUFBQSEkFMWhoKCgoBASiuJQUFBQUAgJRXEoKCgoKITEGVGrSghRCRT10uVjgapeuvapRpGl/3GmyAFnjixnihwQWJYBkiTFhXrRM0Jx9CZCiM1dKQLWH1Fk6X+cKXLAmSPLmSIH9J4siqlKQUFBQSEkFMWhoKCgoBASiuIIzBt9PYEeRJGl/3GmyAFnjixnihzQS7IoPg4FBQUFhZBQdhwKCgoKCiGhKA4FBQUFhZBQFAcghPhICLHN81MohNjmef26dq9vE0K4hRCjfYy/Sgixy3O8T8P4ekCWaCHEciHEAc/vqFMtg2cePuXwHBsphPjB8zffIYQw+Bg/ynPODiHEF0KIwM3Ce4kekGW0EGK9Z/xmIcRZp1SAjnPpriydjj+VdFcOz3n3CiH2ec57/pRN3nse3f2fPCmEKGl3jTkB31SSJOWn3Q/wEvC4j9dHAIc6GTMUyAFWAeP7WoZuyvI88GvP418Dz/UnOZC7VuYDozzPYwC1jzGbgBmex7cAT/e1HN2QZRlwoefxHGBVX8vRVVk6G3+6yQHMBL4B9J7n8X0tRzdkeRJ4KJT3UVrHtkMIIYCfALN8HL4G+MDXOEmS9njG997kQqSrsgBzgXM8j/+JrAwf6eHpBY0POS4A8iVJ2g4gSVJ1J0NzgDWex8uBpcDvenGqAemGLBLQtmOKAEp7c57B0A1ZOhvfJ3RDjruBZyVJsnvOq+jtuQaiu/+TUFBMVR2ZBpRLknTAx7Gr6fxm2x/pqiwJkiQdA/D8ju+l+QXLyXJkA5IQYqkQYosQ4ledjNsJXOp5fBWQ1svzDIauynI/8IIQ4ijwIvCb3p9qQLoqS2fj+4quypENTBNCbBBCrBZCTDgls/VPd/4n9wgh8oUQbwdjnv6f2XEIIb4BEn0cekySpP96HvtciQshJgJWSZJ29uIUg+ZMkaWLcmiAs4EJgBVYIYTIkyRpxUnXuAX4qxDiceBzoLVHJ38SvSzL3cADkiR9KoT4CfAWcF6PCtCOXpalDX+73h6hl+XQAFHAJM+5HwshBkoe209P08uyvAo8jbyzfRrZ3HWL3wn1tU2uv/x4/sjlQKqPYy8DjwZxjVX0Ax9Hd2QB9gFJnsdJwL7+JAcwD3in3fPfAQ8HuE42sLG//U+ClQWo50TOlQAaTldZOht/uskBLAHOafe8AIg7HWU56ToZwM5A76eYqk5wHrBXkqTi9i8KIVTIpo4P+2RWXaM7snwO3Oh5fCPwXz/n9ja+5FgKjBRCmIQQGmAGsPvkgUKIeM9vFfBb4LVTMF9/dFkWZJ/GDM/jWUBfm3e6I0tn4/uC7sjxHzy+BCFENqCjbyvqdue7ktTu6eXIZl7/9KXG708/wDvAXT5ePwdY7+P1N/HsLjx/7GLAjqz1l57GssQAK5BvTiuA6H4ox/XALs8H/PlO5LgP2O/5eRbPiv00leVsIA/YDmwAxp2usvgbfzrJgawo/uU5Zwsw6zSWZSGwAzkC63M8Fgd/P0rJEQUFBQWFkFBMVQoKCgoKIaEoDgUFBQWFkFAUh4KCgoJCSCiKQ0FBQUEhJBTFoaCgoKAQEoriUFBQUFAICUVxKCgoKCiExP8DEFgK0D7inQIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9301c018-ac66-4a64-8efe-a839a349e53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAD4CAYAAADy46FuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAADCIklEQVR4nOydd3gcxf2439nr0qn3LvfeO8a4gW1M7zUQIJBAQoD0kF8gkIQkEBJCaKHXEDCYjm2MwQ333m1Z1bJ6l67f7fz+2JOb2kk6Fb7c+zx6pNudmZ3V3e1n5lOFlJIQIUKECBGiLZS+nkCIECFChOjfhARFiBAhQoRol5CgCBEiRIgQ7RISFCFChAgRol1CgiJEiBAhQrSLvq8n0Bni4+NldnZ2X08jRIgQIb5VbN++vUpKmdDV/t8qQZGdnc22bdv6ehohQoQI8a1CCFHYnf4h1VOIECFChGiXkKAIESJEiBDtEhIUIUKECBGiXUKCIkSIECFCtEtIUIQIESJEiHYJCYoQIUKECNEuIUERIkSIECHaJSQoQoQI0ec43D5e/SafVQfL+3oqIVrhWxVwFyJEiP+b/GLJbj7bW0qUxcDuBxf09XRCnEFoRxEiRIg+5WBpA5/tLQWg3uHB41P7eEYhziQkKEKECNEn2N1eGp0efvjGdiLNJ5UbhdX2PpxViNboUFAIIcxCiC1CiN1CiP1CiIf8x8cLITYJIXYJIbYJIaa20neY/3zzT4MQ4t5Tzt8thDjsH/fRoN5ZiBAh+i3FtXZm/OUrxvzhC4pq7Dx+9XhunJ4JQGq0uY9nF+JMArFRuIB5UsomIYQBWC+EWAY8DDwkpVwmhFgMPArMObWjlPIwMB5ACKEDjgMf+F/PBS4BxkopXUKIxODcUogQIfo7L67Lp97hITsujMGJEcwfnkhuZVNfTytEG3QoKKSUEmh+Bw3+H+n/ifQfjwJKOhhqPpArpWzOYngn8Fcppct/nYrOTT1EiBDfVkrrHQAsvWsmseFGAPSKAMDjk302rxCtE5CNQgihE0LsAiqAlVLKzcC9wGNCiGPA34HfdjDMtcDbp7weCswSQmwWQqwRQkzp7ORDhAjx7cSk1wGaIbsZo157HIWM2f2PgASFlNInpRwPpANThRCj0XYE90kpM4D7gJfa6i+EMAIXA0tOOawHYoDpwC+Bd4UQopW+d/htINsqKysDu6sQfUPlEdjzLsjQijBE2yzfV8rHu0uIDTcyKSvmxHGdf0fhU0Ofn/5Gp7yepJR1wGpgEXAzsNR/agnQwph9CucDO6SUp0bTFANLpcYWQAXiW7nm81LKyVLKyQkJXS7QFKKncdTB83Ng6e2w+i99PZsQ/QS3t+Xu4K3NRcSFG3nrB9MwG3Qnjjc4vABEmEPhXf2NQLyeEoQQ0f6/LcC5wCE0m8Rsf7N5QE47w1zH6WongA/9/RBCDAWMQFXgUw/Rr/jkHvDYIHEkrPkbbHu5r2cUoo/5xxeHmfjHlRyvc5x2PD0mDLdPZURK5GnHa+1uzAaFMGNIUPQ3AtlRpABfCyH2AFvRbBSfArcDjwshdgOPAHcACCFShRCfN3cWQoQB53Fy99HMy8BAIcQ+4H/AzX7DeYhvGxueggMfan//cC0MPg+W/RpK9/TptPqSI0f+SGHhf/p6Gn2CqkqO1dh58qujNLm8fJOjrf8cbh9vbS7k7S1FuDwqZ37dq5vcxIYZ+2LKITpAfJuezZMnT5ahmtn9DGc9PDoQTBFw7duQNQNs1fDcTNCb4HsfQOzAvp5lr+Lz2Vm9ZgwA8+fl9vFsep9nVh/l0eWHT7weGB9OVZOLBqf3xLG/XzWOKyeln9bvimc3oFME7/5wRq/N9buCEGK7lHJyV/uHIrNDdI+81aB64cpXNCEBEB4H17ypCZEXz4WSnX06xd6moeG7u5MCOFquedOPTY/imskZ5FXZThMSf7p0dAshIaXkcFkjw5MjenWuIQIjpAwM0T3evUn7nTXz9OPpk+G2L+GNy+Dt6zSVlPW7EVNZX68JxoT48/p4Jn1DncPD6LRIPv7J2aiq5M45g0iPsWBz+ciramJCZkyLPnlVNppc3hZ2ixD9g9COIkTXKd9/8m99K7rl+MFw3X81j6glt4DP27LN/0Hq63cAYDan9fFM+oYmpxerSVuDKoogOz4cvU4hKszQqpAAWO+3Y8wc1MLxMUQ/ICQoQnSdKr+j20VPtt0meQxc+E8oXA+r/tAr0+pLpJTUN+wCwOuz9e1k+gizUUeNzd2pPutyKsmKCyMzLqyHZhWiO4QERYius/ttMEXCkA7qB4y/Dqb8ADb8G3JW9s7c+giHowCPpwYAn/e7mbto1uB4jpQ3UVwbWBZYj09lY241Zw8O7Sb6KyFBEaJrSAk5X8CYqyAypeP2E76n/a7J69l59THN9gkAmz0Xj6eu7ybTR8wdrgXGbjhaHVD7nUV12Nw+Zg0JBdT2V0KCIkTXWPd3kCokjQys/dYXQW/RBMv/YeobdqLTWRk+7M/Y7fkcOvT/+npKvY7DrUVjW4y6DlpqfHO0CkXAjEFxPTmtEN0gJChCdI0N/9Z+T7y547a2Ki0H1LhrISy2Z+fVx9TX7yQqcjxpadcyIPsnVFQuo6Z2Y19Pq1d5d9sxjDqFaQMDe6+3FdYwPDmSKIuhh2cWoquEBEWIrmGMgBEXgS6AL/f2V8Dngmk/6vl59SFebxNNTYeJipoAQGbmDzCbUjma8xek/G5kRHV7VZZsP8Yl41NJjOi4AJGUkt3H6pmYFd3zkwvRZUKCIkTncdRCUxnEDem4rc8DW1+GgXMhcXjPz60P0QLt1BOCQqczM2jQL2ls2k9p6Xt9O7lewu1TcXpUMmMD816yu300ubykx4S8nfozIUERovNselaLxh51Wcdtc76AxhKYekfPz6uPaY6fiIwcf+JYUtJF6PWR5OW340L8fwirSc/IlEjW5QSW39Pu9gEQHqA9I0TfEBIUITrHnndhzaOakEgZ23F7Z732O35oz84rSKhq11VE9Q07CQsbjMEQdeKYzZaDz+cgPn5+MKb3reDcEYlsK6yh3uHpsG2YX0A0C4wQ/ZOQoAgROBUH4eO7IXk0XPJ0YH1isrXfdQU9NaugUV1dzeOPP87WrVs73VdKSX39LqKjJp54XVz8Flu3XYJOZyEr8//+jqqZ4SmRqBKO1zo6bBtm1GHQCWrtHQuVEH1HKNdTiMD54vdgsMAN74ExPLA+zYKitqCnZhU0tmzZgs1m47PPPmPDhg1kZGSQmZlJeno6iYmJKErb6yq7PR+vt+6EfSLn6CMcO/YysbGzGDniUUym70aeK4Bwf/oOh6fjlC1CCJKjzCdqaIfon4QERYjAaKqAoyth5j0QkRx4P2syKAaoO9ZzcwsCLpeLXbt2MWzYMAYMGEBBQQG5ubns2aNlgjUYDGRmZnLZZZdhtVpb9K9v8NsnoiYgpaSs7EMSEhYwZvTTCPHd2rg73JqACLQAUWqUJaDdR4i+IyQoQgRGsb8OyKB5neunKBCVBvXFwZ9TENmwYQMul4tzzjmHtLQ0pk+fjpSS2tpaiouLOXr0KHv27KG4uJjhw1t6b9XX70SvjyQ8bBBebyMeTw1RURO/c0ICwObS7A1hARqo02IsbMoNLIo7RN8QEhQhOsbngX3va3+nTuh8/8RRUPiNlj1W1/8+cg0NDWzYsIFRo0aRlnYy46sQgtjYWGJjY0lPT2fPnj04nc7Wx6jfSWTkOIRQ0OutCGHE467prVvoV+gUAYBXDawoWlq0hbIGJ16fil733ROs3wZC70qI9qkthP+cA/veg9FXgjmq4z5nMmwRNJZCXWHw5xcEVq9ejc/nY/78tj2T9HpNwHm9LfXuXm8jTbYjRPkN2UIoWCzp2Oz/t/NatUWEWftfNToDSyufFm1BlVDW0LoQDtH3dCgohBBmIcQWIcRuIcR+IcRD/uPjhRCbhBC7hBDbhBBTW+k7zH+++adBCHHvGW1+IYSQQohQ6sj+yLq/Q8UBMITDwj93bYwIf9JAW2C+9b1JQ0MDu3btYtKkScTGtp1yotmQ3Vrp4Lr67YAkOupkpcnYmJlUV6/B7e5/99zTRJi1aP1GZ2CeTKnRFgBK6kKCor8SyI7CBcyTUo4DxgOLhBDTgUeBh6SU44EH/K9PQ0p5WEo53t9mEmAHPmg+L4TIAM4Dirp3GyF6DK9L+/3Lo50zYp9KmD/Zm73/6aGPHDmCqqpMmTKl3XZGo1aYyW5vmTq7tnYTQuiJihp/4lhq6tVI6aGyalVQ5/ttoLM7ipOCImTQ7q90KCikRnNifYP/R/p/musWRgElHQw1H8iVUp6qf/gn8Cv/WCH6I65GSBgOxm6kWDghKPrX6rqmpoYvvvgCo9FIXFz7mUub25SUnP4xl1JSUbGM2Jiz0OlO/o+s1hGYTClUV6/tkbn3Z04KisB2FGl+QXE8JCj6LQHZKIQQOiHELqACWCml3AzcCzwmhDgG/B34bQfDXAu8fcqYFwPHpZS7O7j2HX7V1rbKyspAphsiWKg+KNqkCYruYIrQfrv7V8W3/fv343a7+d73vodO17GHTlpaWgtBUVu7AaezmKSki047rhnCZ1JbuxEpv1tRxydVT4HtKCxGHbHhxpCg6McEJCiklD6/+igdmCqEGA3cCdwnpcwA7gNeaqu/EMIIXAws8b8OA36HprLq6NrPSyknSyknJySECpv0GqoPnpkBjprOu8SeicG/0vYEVvGstzh06BDJyclkZGQE1D4tLY3GxkZOXbAUFDyDyZhEUtIFLdrHxszE662nsXF/i3P/l2mul90QoKAAbVcRiqXov3TK60lKWQesBhYBNwNL/aeWAC2M2adwPrBDSlnufz0IGADsFkIUoAmgHUKILirBQwSdTc9C1WEtmd/Em7o3lt6kGcPr+ocpSkrJtm3bOH78OOPGjQu436hRo9Dr9axcudKfsmMHtXWbyMz8AYpiatE+JvYsAKqqVwdr6t8K6uxavWyrKfBEf6nR5pCNoh8TiNdTghAi2v+3BTgXOIRmk5jtbzYPyGlnmOs4Re0kpdwrpUyUUmZLKbOBYmCilLKsKzcRogfY9rIWUT3rFyBE98YSAoYuhIOfajEZfcz69ev59NNPyc7O7tCIfSpWq5Vzzz2XI0eOsGPHDvILnsFgiCEt7dpW25uM8cTFzebYsZe/U95PO4rqABidFrgrdWq0hZI6R6teZSH6nkCin1KA14QQOjTB8q6U8lMhRB3wLyGEHnACdwAIIVKBF6WUi/2vw9A8m37YA/MP0QV8TW5qlxzBXWIjYnY61pmpiDOFgeqBYedDRFJwLjrmKti/FI5+qY3bRzidTlavXs2IESO46qqr2s3f1BpTp07l0KFDrFv3X8aM/ZqBA+47zYh9JkMG/47NWxaTm/s4I0b8pbvT75d8uqeEzXk1KAJMBh1LdxwnKdLEuPTogMdIi7Zgc/uod3iIDjP23GRDdIkOBYWUcg/QIhxXSrkezeX1zOMlwOJTXtuBdl1K/LuKEL2Aq6iBmrcO4rN5MaaEU/9pHvXL8hF6hbDxCcRcNgRKdmlqInsNSNn9HQXA4HMhOhNW/wWGLNRSe/QSUkpKSkpQFIVVq1bh8/mYOnVqp4UEaPEUl1xyCStXvomUZtLT21fLhYcPIiP9ZoqOvUxGxvexWod19Tb6JfuO1/OT/+4kwqRHUQQur4+kSDMv3TzlRHLAQEiM1KrhVTa6QoKiH9L/8imE6DEaVh+jYWUhuigTiXeNw5ASjn1nBZ5SG03rjmPbXKYJivdu0TpEpARHSADojTDnt/DhnVC0AbLPDs64HbB//36WLFly2rG4uDiysrK6PKbLVUhsXD6qbwEGQ2SH7bOz76L4+JvkFzzF6FFPtty9fYv5cOdxTHqFb347j0hz12tehxk0e4bD893yEPu2EBIU3xGkKmlYXoAw60j6yXiUMO1LHT5RUy2pDi/2beV4Ku0Y9BakMQJx64rgTmLYYkBA4cZeERRr167lq6++OvE6JiaGefPmMWbMmG6Nu3vPowihY+LEXwXU3mCIJjvrLg7tfQO16V3GTb+mW9fvT+RX2RgQH94tIQEn80I154kK0b8ICYrvCEIRoIB1RuoJIXEqYRMScR6sofzx7cSGp2P25SOdHpQAy04EhCUaEkdA0cYgDno6LpeLpqYmli9fTk5ODmPHjuXcc8/FarV2SdV0JpWVh1CULfh8s4iPHxBwv+zsu/j6P0nk1cWRnNxAUnbHO5FvAwXVNoYkRnR7HLdPqyxo0ofSz/VHQu/KdwRfkxtUcB5qPaOpeVA0iXdprqIN9ktRsON5/+HgTyRjGhRv1eI0QMso+82TQUlD7vP5eOmll/j3v/9NTk4OGRkZXHzxxURGRgZFSADs2vUoIBg/7ped6ieEgqpq6pWPn9hJWV59UObT13hVicnQ/f+t26sJCmMAgY8hep/QjuI7grdaS7jmKW07OlofZyHhznHAOOTL94C7B5K0DZwN21+B3K9gyHnw5YOw8SmwVcCCPwU8jJSS/Px/UVu3hejoyezdU8nBgx5cLiuJiYlYLBauvfbaE1lfg0FtbSGI9Xg9k0lOHtnp/qoPotOqkZ50Pv7XLi78yVhSh8QEbX59gcWgwxGEetcnBEVoR9EvCQmK7wj6eC2fjnFA+yoPU1Yk0tEEeJDmHijfOewCsCbBludhwDmw/TXteG3gKchttjwOHvoN9fXbAair20xEJGRlD8BkvJPLL7+8RwzGO3Y+ihAqo0f/otN9VZ8Pjz2CmNEVzL58Ih/9cycf/XMX0clhJGZHMuKsFFIGRmkqwm8R8VZTUNKD25ur4nUiSC9E7xES398RhF89YB7WdirtZrxH9iCERKR0ftXcIXojTPieFk+x+21wN2opPioOBNRdVT3s2n0r9fXbCQsbzORJ7zNu7AuoqkJUpJ4rrriiR4REU1M5Pt8qXK4xZGS08ArvkPraCpA6rNFmwqNMXPbziYw8O5WIODO5Oyr44O87ePW331C0v/9l2G2PwYlWciuauh0o1+TSBEV4gOVTQ/QuoXflO4Lwb+mlR+2wre/IZgyAbvjkDtt2iQk3aHUuPrkHTJEw5QfwzRPgcYDB0mqXxsb9hIcPpr5+N07nMUaPevK0/EouVxqK0nNR39u3/x2dzsOgQfd1qX9dpZZ0ICJWM/xaIozMvl6LqXA7veTvrmLHikJWvLCPK38zmZjkYHoR9ByDEq3Y3D7KGpykRLX+3gWCzeXFYtCFvJ76KaEdRT/g8W2P86s1v+Jg9cEeu4ZQBOgEeDsWFFTkIjGiG9DNrLFtETsQxt+g/T14PqSOB6lC5aEWTaX0UVz8Flu2Xsyhww9SW7cJEMTGnn1GOyMCV49M1+msw+n6DIdjGIMGntOlMRqqNSeCqFbSmRvNeoZNS+bCn4xDZ1D4/Nm9uByBJ9TrSwYnWAE4WtHUQcv2aXL5OhWgF6J3Cb0z/YCvir6iqLGIZQXLmJk2k9vH3M6kpM6rNzpCGJSAdhSgItEFzVOoVS58Qqu/PXTRyeJI5Qda1OQ+cuSPFB9/A4DSUi1wLiJiFAbDmXmEzCDqemSq27b/A73eRUbGj7s8RkNNAxBFdGLbKVEiYs0sumM0H/1zF+/+eQsjZqYy+pw0zOHdi1HoSQYlajufoxVNzBrS9ezONpe3U0kEQ/QuoR1FP0ARCmennc1PJ/yUg9UH+f7y73PTsptYV7wuqNcRBgUZyI5Cp9NW+D2J3ghTb4foDIgdAHpLCzuFw1HM8ZK3SUm5ijmzD5yo+ZCY0DJXlBAmhHAHfZpuj52mpg+w27MYMXxxxx3aoKnGAcJHVFz7CZJTh8Rw9tVDaKhysvmjPD5/Zk+Xr9kbJFhNRJr15FZ2b0dhc3mDsqOw2+0cPHgQtzv4n4XvMiFB0U8IN4Rz+9jbWX7Fcn479beU2cq4a9VdLC9Y3mr74sZiviz8ErUTD3Sh73hH4frfY5hK3wLRi5tNRQcJw6D89LoNBYXPAgoDB9yDTmdi1MjHmTxpCVlZLfNLCswoPSAodu54CoPBTmbGD7tlJG9+m0oLDnfYdvTsNBb8YBTj5mdQmltP5bFGfD61X2ZWFUIQG26koZuqsqYgCApVVXnllVd45513WL9+fbfGCnE6IdVTP6DOVUeUUVOlWPQWrh9xPVcMvYJzl5zLb9b+hv1V+7lhxA0kh2ur0dXHVvObdb/B5rGRGZGJUWfE5XOxeMBiLhl0CRmRrRfiEToF6WtfUBgOPoEQPlyj78cc1LvsgMSRkHuyvrTTWUJp6fukpl6N2ZwCgBA6oqImttpdKBZEkI3ZXq+L2rq38XpSGD37ym6NddbFcynavY2Vr+zmxgcHYmjDaA/aw3fI5CSSsiPZveoY7/1tGwJByuAossfGM+KsFIzm/vPV1esUfGr3hJjN7SUxonufuOLi4hNFpTZv3sxZZ52F2dyrn+L/s4R2FH2MV/VS56ojznK6kdOkM/H6+a+zIGsBrx94nYXvL+Tur+7md+t/x91f3U1mRCY/nfBTsiKzSI9IJ9Wayn/2/IfFHyxu2ygewILYq2TjsYzFfOVPgnB3nSBpJDSVg01zD9V2E5Cd9aOAuhsM4SiKitcbvCDBPXtexGBoICnploBKpbZHdHwK0y4Px16VzKr/vhdQH0uEEb1BQfVK4tLCKT5Uy/p3c3jh3rV88PgOjm6v6NacgoVeEXg6WIB0hK2bxuyysjJefvllAK666ipcLhfbtm3r1pxCnKT/LEu+o9S56gCINbeMbxgQNYBHZz/KPU338N6R91ias5QaZw2XDb6M+6fdj1l/+mpp9bHV3P3V3RxvOs6IuBEtLyaADhZ+0hCO8NZ1OO/j7z2ELNxE7I3PEZbU9UysJ0j2J+or3YUzfQQlJUtITb0Kszk1oO4WczQuNzQ0VBAbm9nt6aiqj/KK1/D54ph9zs3dHg9gwux55O16i9xNKRwatYHhk89qt73BpOOmv5yF3qjDYNRhq3ex9n9HKM2tpySnjrLceqKTLMSndz/XUlfx+FQKq+1MH9huJYEOaeqGMdtms7F0qVZsc9GiRYwaNYodO3awceNGpk2bhsHQf50Bvi2EdhR9TLVDW0GfuaM4lTRrGvdMvIcvr/ySFVes4OGZD7cQEgDDYjS//HpXG3mEhNDqS7SHTg9qx7r+yN3Pkd64gapnLg2O7rzZ26lkB3n5/wIgO+vOgLuHWzX1VE1N4BHe7bH/wJsYjdXExtwQ1DQgF/zgYkyRNax7uxSft2NVmcVqxGDUHqDhUSbO/+EYbn30bL7/15moquT44bqgza0r7C9pwOHxMSW740DO9rC5vF0Otvvkk0+orq7me9/7HtOnTwfg7LPPxmazsWvXrm7NK4RGSFD0MdVOTVC0tqM4E4POQKq17RV2tDkaOLlLaY2Onulq5HD03kKkvW0vlqqdXxKhs+NQTWSKPEo++1f7gwaCOQrih9JQtpbS0vfJyPh+wLsJgOgoLZNrXX1+t6cipaS4+HmczigmTw5uYUZzeASj51pw22I4ntexYbvNcazaKtkThDxL3WFbgRYfMiW76zmrVFVid3dN9eTz+cjNzWX8+PEMGjToxPHs7GzS09P55ptv8PlCNS66S4fvjBDCDKwFTP7270kpHxRCjAeeA8yAF7hLSrnljL7DgHdOOTQQeEBK+YQQ4jHgIsAN5AK3SCnrun1H3zJO7CjM3du6g2YIN+vMbQoKIUSHqiclawyiXMWddwTj6NYNx/Yv/oJPCsQP11D73AKitjyKeu6tKOZups5Om0QeKzEYYhiQ3bmYhdjYQeTlQ1NTUffmAOTkfIzRWIZefxtGY/CNoYkZmUA1NeXFZA4d3aUxdHoFvUHB2dS3Nci3FtSQFRd2okJdV7D58zxZOykoysrKeO655wBISUk57ZwQglmzZvH222+zb98+xo0bd9p5l9NJY2U5qs+HUBSEEOgNRgxmM6rqw+N0nvg5vPk44bHZJA/wf0eFVt9FSu23qkqklEgVVJ/2t+qT/jbNf2teWdpvebKf/2fo1GQSMvtOhdgRgbwzLmCelLJJCGEA1gshlgEPAw9JKZcJIRYDjwJzTu0opTwMjAfw19w+DnzgP70S+K2U0iuE+BvwW+DX3b+lbxc1Tm1FFmvp3ta9mWhzNLXO2tZPCjrcUigJmseUmr8bWhEU9tJcUm07KIuYTFraCCpn/I6MLb+m8u37SLjlpS7N2dNYSGPRMqqUHVRHwKCEK9DrO/eliYhIR0pwOku7NIdTOZr7HIqwcNaMu7s9VmtExEQC1djqu/eQj04Oo7adbMA9jZSSbQW1zBnWveSRNpe24u/sjqLZWD1q1KhWi1ENGTKExMREVq5cSU1NDS6X60S9kpycHAw1FZjLA1tY6C3noDcHN6WNogiEIlB9KlXFTVxyb4uK0/2GQGpmS6BZD2Hw/0j/T/MSMgoo6WCo+UCulLLQP+4Xp5zbBHTP//BbSo2zBoNiIMIQvNWEbGvbEIDXk378Ofg+i0XsfxcuuqXF+bJXf0i2UAm/4EEA0s//IYUbnyE5/yO8tr+jDw9cBeGpz2PbhoXYTZrHjAiXJDhjSc+8NeAxmlEUIz5fOB539zyBioo2YzIdAS7BYumZFZ4mKMDRTUERmxpOyZG6IMyoa+RV2ai2ubuldoJTEgJ2wpjd1NTE9u1a9uCrrrqq1TaKojB37lzeffdd1qxZg9FoxGQyYTKZALBERnLRjff7dwMqXrcbj8uFolMwmMwYzBYMJhPv/ekP6I1OrvrtZG2dJUEoWlocIQRCOfnQb/03J//WCRQhTssSvPGDXHauLMJp8/TbKPyARLh/N7AdGAw8LaXcLIS4F1ghhPg7mq2jfRcOuBZ4u41zt3K6iurUa98B3AGQmdl9b5b+xvs57+NRPUHJeOryuaiwV5yIt2hBAKonYTDiTVyAsfw9pKoiTknjkb/8ZbKdW6mMm0XSyFn+IQXGub/AtPZuij9+jPTrHgl4vhUH/oXdpJLNeKLjZxE18Gr07dhgOkLKaFTZveyr+w88gU6nY+qUn3VrnPbI2btb+6ObQY0xSeEc2VyOx+07YfDuTU7YJwZ0bzfcXM8irBPG7KVLlyKlZMGCBe22GzFiBPfffz+Kopxwcc47eIDX33mXwSNHM2RqR48tUPRhWMJ9JGb1TFXCgeMT2LGikIK9VQyfntJxhz4gIGO2lNInpRwPpANThRCjgTuB+6SUGcB9QJt6ByGEEbgYWNLKud+h2TjeauPaz0spJ0spJyckdD2XTH+lTQ+lLrCxZCOqVJmY2LptIRDVk/R60Vd+hRTm04REU0UJEWsfwEkYCbe+elqf5Dk3YveZIG91p+ZbWbsWs0swcM4S4sbe2y0hAaDTxaMo9V32wqqoOIpevw2YQXR0erfm0h57tq8FIGXokG6N4/OpIDR7RV+wtaCW2HAjA+O7l+m2OWNsZ4L2SktLGTduHGed1fGD3mAwnBYHk3dISxUzYOiwgK6lN4bhaOq5ioSJWRFYY0zk7azssWt0l059wvzG5tXAIuBmYKn/1BJgajtdzwd2SCnLTz0ohLgZuBC4QfbH/AQ9jJQSi97CvIx5QRnvgw3vEEUEUxKntN5AiI69nurr0MkKPPGnr9RKXrqNeGMjvkV/R7HGnz6solAXNoQY51FkgB4mXnsFNcZ6EvVDTxNI3cFoTMZotGG327vUf+fOf6IoKuPG/jwo82kLQ5imF6841rV5NlNfbscaY0Lpo9Tc2wpqmJwV0+3dcJh/N9SsggoEg8HQ5QVByfHjICWDRwZWb8Uam4KrqaLHUqgIRTBgXAJFB2rwuPqnh1aH31AhRIIQItr/twU4FziEZpOY7W82D8hpZ5jrOEPtJIRYhGa8vlhK2b1vzLeU3LpcHF4HszNmd9y4A+oaalhn38TsmknYPi/SamTDaR9uoR1odxzh0gzhauLJ7LWO8nwGOzdQHjmViBk3tNpPDphNuM5F3b6vAppvU9HnSEUQkzA3oPaBYLGkotd7qK3tyFzWkoaGKiRf4/WOIilpbNDm1BqW2EOguCnJrevWOBVFjSRmdk0d4nQ62bZtGx999BGvvvoqH3/8cacEbEWjk4Jqe7fjJwBSoy0YdQo5FY1ttvH5fFRXV9PU1ITH46GhoYHY2K5du7q2Fr30YQkLbCcUl56FVBuoPt5zRaUGjo/H51EpOtA/C1cFohRMAV7z2ykU4F0p5adCiDrgX0IIPeDEb0cQQqQCL0opF/tfhwHnAWc6pD+F5nK70r8i2SSlDCxfw/8RNpRsAGBGyoxuj/XJxvfxKF4WGeZg21iKbUsZpoFRuHLrsZ6dRvTiAQEZs0VEvFbbofJk1lJXwXYsAjxDL26zX9i4C+DQ09j3ryRm3HkdXqepWvOktqbN73hSAWINz6CxEerq8klP75xaZ/v2pzEYXAwc0PVU4oHQ1FSOJawcJbyChtKuq1KdNg/1FQ6Gz+i8Ttvj8fDyyy9TUVGBxWIhNjaWXbt2cfjwYRYtWsSIESNwOByEh4e3mWp+W4G2oOiufQK0OtlDkqwcKGlo9bzD4eCll16iqqrqtOPR0dFdup7N7cFqNgXcPnXIIHI2QcGeHOLT4zvu0AVSh0RjCteTt6uSQRN6oARxNwnE62kP0MJvS0q5HmhRNEFKWQIsPuW1HWgRJCClHNzZyf5fY3PZZrIjs0mxdt+A9UnJ52Sracy86zJ8lQ5sW8txHKgGVdK0rpiwsfGBGbMjYnCHjUWp2nXimKeqAAB9fHab/aKHTsel6pHHd7XZRnXWU7HrT1gTZ1Jevxa9XmKKGx/wPXZEZFQ2pWXQ2MlYCo/Hjd3xMTolhezs9o2j3eXIkU8RApQIL54SIw67C0tY4A+tZnat1O4xc2TnH9RfffUVFRUVXH311YwYMQIhBGVlZXz44Ye8//77J9plZWVxww03YDQaW4yxJb8Gs0FhVGpwDLyjUiNZdVBT75ypysrNzaWqqorZs2djsVgoKCggNTWVsWM7v/Pzej14FT0xkWfWM2mb7HHDWPMGlObkAt1f1LWGolMYMDae/N1V+HwqOl3/ioUO5XrqI1SpsrNiJwuyuv9gOlpwiIPKUX4cexuKoqAkhRN94UCiLxyIr8lNxVO7qHr9AEqYHkXR3O98Li+Hn9uLrHcx4v6pKKcYRNWIoRgd72tqKiGQtVpaDEPy0DbnIHQ66pQkLI1HW79fVyN7l0+hKtIHBUvBDHGOiKDZJwDCwzUDtNfbOaPgrt1vYjbXERfbvVTigVBa9il6vZGh40ZxoMRGzu4ixs7o3O6nJKeWXV8eY+CEhE554thsNg4fPszGjRuZNGkSI0/R0ScnJ3PHHXeQk5NDQUEBbreb7du3s2LFCi666KIWY31ztIop2bEYgvRAG5kSybvbiimpd5IWfXpm3ZKSEhRFYdasWej1+hNpOrpCTXk5CIE1MnDX57j0FIRiovpY++lhfB4vSx55hrrSfCIT0zn7mkvJHDWo3T6nMnB8Aoc2llFyuI6MLiwAepL+Jba+Q3xd9DWN7kamJLdheO4E7299B0UKLp3e0p9cZzUSd/MopNOHt8yOt9xGQ2EDOX/cTGRpE1F2D0UrCk60l6oPXe1uJGZtBwLIpgo8qkJYfPteSc64scSKajy1pwe92cu+YePqyZqQAFJdqQwKW8jIs97t5p2fjsWsuQWrvpqA+0gpKS97A4/HyujRwUn+19Z11q37OWbzHnTKbEZN1oRD3v7AAwSdNg+bP8njk6f2YIkwMHlxdsB9S0pK+Oc//8nHH39MXFxcq26liqIwbNgwFi5cyEUXXcTMmTPZvn07+/efXiekrN5JTkUTs4YETw0za6imhvtgR/Fpx2tqatiyZQuDBw8OSs6tmiptERHZiR2FEAKzNZnG6uJ22739h39y/MAX2BtKKD28hvf++CuKDxcEfJ2MEbHoTTpyd/U/76eQoOgiywuWM+a1Mfxhwx84Wtv6KrotvKqXZ3Y/Q3ZkNguzF7bbtqKmhId+fRWv/fcvrZ6vqihnaeOnzBSTSU5Ka7WNMSWcpHsmaOkE7F6qn9mNyePDMSkJG+BdfxyfU/M4UcuPY/Dsx5N0yirSXoPDZ8DcgfHPMHIRQkDtptMFQP62e3ArHkZbr2X+vFxGnL+O7OnPYIwJzD0xUPR6TYWjysA9R44e/RpLWBHW8ItP9D+Vw4c/Z9u257o1L6/Xxdq1t+H2fIjDPpnZc/5FYlosUu+h8ljgleG+ev0g2z4rQKcXXPbziSRkBLYqllKyYsUKDAYD1113HbfffvuJoLP2mDdvHmlpaXzyySfU1590D12Xoz3Izh4cPHf1QQlWZg2J541NhaelLM/NzcXr9bJo0aKgXKfOb+eIiu5ckGBkQjpuR4XmktwKhzcdpPzoWhIGTOe+t/7Hlf/vn0jgg78+gtsZWC13vVFH1shYjm4rx2X39KtCVSFB0QU8qocHv9Eik9/PeZ/LPr6MYw3HAu7/4t4XOVJ7hLsn3I1eaX+VVFh8GGuBg6qPvmHZitdbnP/zij/gVNzcM+cX7Y5TV1POmvIPOVC3AadOYL15FEOuGopuVhpmCQVLNWGnJKYjpe60YDDhqsOFuUM1Ufz0y/GoCu4jJz2f7EUrKbfUkKYfR9LUP7fbv7s0G15lJ/zxc44+g89nYMKEn7Z6vvj43dQ3PNblOfl8Hr5YeSle3xqaGueycOGbGPwCyRwDzprA5mqrd5G/p4qMETFc/otJRMa3XvjI5/Oxf/9+VqxYwbJly1i7di0ffvghhYWFzJs3j2HDhgVczEen03H55ZejqipLly5FVbWH5MbcauKtRoYnBzdy/ZaZ2ZQ3uPh878ldVllZGSaTiZiY7kV/N9NQpxnho+I6l1stPjMbpIvSI63vKtb+9y0QOi752Z0IIcgaM4ipl/wAt72EJX96MuDrDJyQgMvu5cWfreO5H6/mf3/cgi+Q8sU9TEhQdIED1Qewe+08cvYjJ3YEx5oCExT7q/bz3O7nWDxgMQsCMJyWlhec+Hvvq+/w7HO/we7SVqEfbVjCl+p6bo28jmFZbfuE532xkf8+8HPKHUc41LCHoX+YTsJI7Ysy6PwBNOgE6p5KvC4fuJoQwgf+TLQAOncjHiWsw7kawyOpF3Hoq7WsqNLnJXf//QgVsiY+2mH/7tJsX2gzhckZVFUdxWDYhWAm4eEtV8dud/fzKO3Z8xIm0xEE13HRRc+fVhshLj0M4TFTmNOxO+/uVcdAwswrhxCb0nJnJ6Xk4MGDPP300yxZsoStW7eya9cuvvrqKw4cOMDo0aOZNKmF70mHxMXFsXjxYgoLC1m9ejUARyoaGZkaFfT4jTlDExmUEM6zq3NR/cK+uLiYtLS0oNmOGhs0z6q4xKRO9UsfrqkKC/cdanHu4Dd7aCjfTdqI2UQlnhRAs647n8SBsyjLWcOG975o0a81Bk1KZM4Nwzjr8sGkDYum+nhTv4itCBmzu8CKghWYdCbmZMxhdsZs1havZVn+Ms5KbT9K1O1z89v1vyXeEs/90+4P6FpFR7Uo0gse+SPvvPAIuq/38cSGa9CnJrN08B5GMpi7LmiZbqKxqIK1L7yMT3rJPboVqzkGZASKzoTulFQJQhGEz0xDt7aYos/yyV6QggCEOBn8ZPDW4zRnBzRfT/QQYmq20FCwjH2HforDpDJQmYopqntRyD3B7t1PIoRk1Kh7Wj2fl3dyZySlihCdW1e5PXbKyv+DqsaxcMGDLVxNpy0axtJdu/n0mV3Mv9nN8PHZrY7j86qaoADi0qwtznu9Xj744AP2799PQkIC11xzDcOGDUNRFDweD3q9vlsP2vHjx1NQUMDatWuJSUhi3/EGhiS2nEd3URTB3fOGcO87u/jiQDme/K2Ul5dz3nkdu1sHSnOsSHQndxTZ4zRHjk3vPcnxwwex1dZhjY2hoaqcutJDCF0Yi3/cMjfaVb+/h+fvymXTe/8he9wwUoe0X+RLp1MYNUtTIUskxw7Wouj6JqDyVEKCogvUOGuIt8QTYdS23hcMvIBPcj8h2hRNnDmOOEscseZYLHoL+6v3o0qVnNoc3D43+fX5PDHnCaJMgRnTaouLEWGS4YMm8OBfl7B89X/Z8ME7ROWXMzIsjgduegyD6XT3xdqcYt774/+jwaXpY1NiB3PJww/w+u//jNfV0lc9Y0EWR9cWI3eUI85PREqB9GqrmKbCPUTq7NQHGoSWPAY7W9mZ9xMwwiDdDLJmtVSZ9TUuVxMe7ypUdTgpKa3fW1nZ2hN/FxV9Q1bWrE5dY+OGhzAaG4iN+RN6fctkb6lZiYy7KIFdn1Tw5YuHGP5Udqvj6PQK4+ZnsHNlEXUVdqITT+7ubDYb77//Pnl5ecyfP5+zzjrrtHQVwaru1ryr+Oj9JYQxlnpH5116A+HCsSn8a1UOz6/czdj67YwZM6ZbXk5n4vX5QFXRd/L/Ygo/eb/H9q4EoMavhYpIGMvcm28gMj66RT9zmJlLf/lblvzxF7z/yCP88LknMQZgHwItZTnQL1xlQ4KiC2RGZLIsfxn1rnqiTFHcPuZ2tpVt49X9r3bYNzU8lXPSzwn4WrK8AXHK6m3RnOuZN/lS/n3b1dxguIDs7NPDUSr35vL+3x7A5XNw0fd/rflnz5uGTq9HoIBsqe9U9Aq+YbFEHK6h5tPVxAmJSNPy99esfhkrEDHt2oDmqx8wgZ1JmhBM1c2jKfseqpoaSIiMDvieu4oQAlUVKErHqSB27PgPBoOTlOS2M9Xa7btRdGYMBid5eZ+dEBT19SVs3fYwNtsuQJKZ8WMmTLjptL6bNv8bt2cpHs8oxo9v+383a/F49nz9McLXMlbhVMbNz2DP18V8/K9dXPbziVhjTOzdu5dly5bhcrm45JJLmDCh59JUG41G5s6dy9KlS5lqrcEU27EqsivodQo/njuYzz9cQq2wMGfOnG7XKz8VrYhR543EBqOJsKhoHA31/PDZd3C7HHz496eYsHAh489rX5BljhrAlIt/wNaPnuadP/yDG/786zYDGU+l2dYW2lF8S4kzx6FKldy6XCYmTSTVmspHl36EV/XS6G6k0d1InauOOlcdadY0wgxhJFoSyW/IJ8GSgEEX2GqmrqkGSyOYx53uzWS0hpEYmcWR/RuZUX4jYUmaoa9kyz4+eOKPqKhc8bM/kD7l9JWyUBRkK4ICIOuigVQdqsa732/UTtYy9ZoKVlHviyB69JyA5hwxcg74a9qX+L6C3K8o9Joocc1i0ojvM2HQ9B6NVZBSh8/XvpeJqqrU1L6HEHEMH35Zq228Xjd6QyFSnYHbfRCjcQkrvvgAn8+IyWRHCDAYItDr7VRW/Yny8rNIStKE9vGSLdhsT+B2RzNn9ovt3q+qqvgceiIS2n8YhkeZuODHY/noiZ1s+noX+dV7KCkpIS0tjUsuuYTExJ6P5o3zq2uyvcWoZcVs2DeEs0YHHicQKJeOT+Xh9zJokBb+YAhu4ShVVQNJUNAq0ckp2Ovr0JshPCaOW/7+YMB9z7n+fI4d2E9Zzmpe/cVDzL35BrLGDG5XYKg+iRCclpK8r+j7Pc23DJvHxgt7X2Bw9GDGxJ8slqIIBaPOSJwljuyobMYnjmdOxhyGxAwhzZqGQWdgaMxQYsyBe2/sOvANCoKMQSNanDvnlttweBpZ+vsHOLpsPZ/85k+88/j9CEXh6t890kJIACg6HaraumEsLN5Ck0mP0ePPNRORgOpxE+M7TkPU6BMxFR2hhJ18YJW45+Kx3k8js0g3r6G26EaWLD+XdXveaXMe3UVKPV6vo902hw59gtlcQXT0FW1+UQsL16LTeYmNnYbZPBMARVExmew4HDFYLD9i4YLtDBn8JDqdj8NHtHpcNlsV+/f9E4DRo14kPLz9B3hxQRk6r5nErI51/unDYrDHHOWbvcux2Wxccskl3Hbbbb0iJADS0tL4xS9+wTU33IwqFD745PMTnlDBRK9TSPOrcX751jdBHbum5Dh0cc6JA7SFQOHuHV3qf93DPyN91AJqj+9g6SM/41833cRr9z7Ajpc/wWNztmiv+lSUfqB2gtCOolPUu+r55ZpfUuWo4q+z/hrwzqCr5BzZCcDoES23tllnTWRewe189dELfPTqX1GEjqGDZzDrh7cQmdl6PQpFp0O294BODEMp1fzlRUQidfu+JlbxoWQHlraguKyGm59aydjsBYyIqOW2K5/361dvo6apmhVbXwd1Ce6q+3l3+XNkZv+es0Z2nDlXSkneFw9QXvEprsgmwptSGTz2fmLHtIxBUVULqtp2cjmAgoKX0OlNTJxwZ5ttSkrWAJA94Dxiom8j5+hchg29AI/HjdFoOrFLyMo6l0OHY3C73mTTJjf1Df9Dr7fjcY8nK6tjVdDh7ZqReuDYjtO4CCHwmWyE6aO5++6fBCUArbNYrVZGDLGSNHwy1Ye28O6qLVzbgeqlK0wfmsLBygK+LAxuqVeft+vjZY4ex67ln1C0bw9Dp5/d6f6KonDNAz+lLO8KdixbTdG+HVSV7mBtjWTzRgOJhhrGLRrE4IunaXP1SUQ/UDtBSFAEzCe5n3D/es1T6ffTf8/k5OCWRWyNytyjKAbJwMzWXV/HX38xyaOGUV9cRtrU0VgT2vfksERGU1dqw+v1ode3VHXoY0yIMk2/L0xm3PlbATAN7FhQfLl+J/d9fBQHZi5UFnH71RefpnKJtcZx3dz7cLh/zLKtb6B3/Yfyop9RlvoFydEtV8RSSur3rebYvheoFbvwJLowmI2EN6TQFFvMzpK7SNoymZE3vopispzSLxJk27UDysoPYDQdABZgbqfGd2PTDiCahHhtFTliuBaAaDKdrgpRFD1W68W43a9hs7+M251ISsqDjBl9RYf/M4Dc7dVIvcKQsRkBtTcbLbjcjj4REqdyx+Xn8cBf9rBj29agCgqvT8Xp9fG/LSfdzY+WVDM4tfs15QESswZSUtu12hJZY8YDUFGQ1605JA9MY/GPb0D1XsM/r7+ENFGONbyCPGc67k9yTwgK1SfRhQTFt4t/bP8HAH8++89cPKjtLKrBwmFrxHSgBke2FaUdY17yuGEkjwsswjk2NYnSw17KjpaSPrxlYR7p8gEepBQIvcBXcQhVQsTglkLxwMEc3lu7n6QII0cqbHxYasSK5OUrBnLOtJb1i5uxGI1cPvM29uSMpKLwRvZ8fC1J31vVQo+/+7+XUp2yDxLAXBlJUuMChlz6KIrBiKMqn32rb6N8wDbql57F6MlPEzVEc01WRBQobScF3LvnXwgFxo5tPcAOQFV96HR5qGpgxuFpU3/B9u0WzOZ4zpl1HYYA9eqbv9yHr85MynilVcHdGhZzGE3OuoDa9iQmo4GIlAG4jx/keGUdaQnR3R7T5fEx4Y8rsfsr3ikCVAlf7s4PmqAwGPRIRbSafLAjjGYzOoOBuvLu12UHcFRoeaeSByQw6w838fpt72I0nNzxqz7ZLwzZELJRBMyz5z4LgOiyKaxzfPrCEwDExgevNGLacM3wWLD7cKvnvZUOvGoGQkh8+btQ6oto8lkwRZ5MUGa32/ntE//jklf28XK+gb/skbxfFsYQk4PlvzqvXSHRjJSS0gdfJfJTHbr0Qo58fPpDu/bwKqpT9hFZnM600Z8w87qdDLvkCRSD5hlkiR/A5CtWkW27FldkI9uPfo9Db/8IKSU6XRx6Q1Or6Q/s9lpUuRaPezSJCcPbnF9x8Wb0ejfR0YHl4TIaw5gx45dMmHBLwELC7faw9eNjSKOLC24KPCOp1RqBT7hw2AJLC9GTzJ42AUXAp+u2B2W8T/eUnhASAKt/Pgs9PrbmVbXTq3MYDAYQCh5X1/5/1phYnI2tp0PvLE0lxwGwREUD4BJmTvWcVX1qnxWlOpOQoAiQoTFDiTRGsq18W1DHbcsYOPfGHwAg41pP1dAVBk4YBUBpzpFWz8tGNz60RG9qzg70jkociubqun1fDr956gPO+sMnvF0WwbBwD+/cNJq3bxrD6rsmsOyha0iJD8xQ/+Vj/yFzx1ryfdcRXpxAccTnbH1jLpXb3sfndnBky+8RLhi94BWsya2r3YQQDLroz0wZ9wGW+niOJ62kaNmjGI3J6HRebLaKFn127nwOvd7NwIG3tzu/Y8VaoF1W5rkB3U9X2PTFXoTbxNgFyZg7kWY8JSUZBOzf0fp72JvMHDMYNzqOHz/e5TFqbC6cHh8/+e8Ofr5k94njf7tiDJnxkaSavRysbGno7SrNcSV2e9ei7s3JcUgpefhHF/PHX17Nfz/7N2obnoQdYS8rAyAsLh5VVfHoLFgsJx/J0idDxuxvG4pQmJg0ka1lW7s9Vl7VDr7Ke4cNpVvY11BFisnM8+e/Q1LEwBNt4mKT8AlJff6xLm2TWyM8KhKdMYaakoIW5w7n1rBF/Ygr9csA0E09H922h9nvzeb8X79DjbACRgbobPxqdjzXLVzcpTl5fSr6996mKGkgVzzxIDjuY//Ht1KVuJs9Db+C1b+CNEiuPgdL8sAOx4tIH8OUK75m44fTyDO/QJjnSpoUqK4+itV6Mk2Dz+ejvn4pkMSQIee3O2ZDw3aktJKSMrrT9xco1RWannzM1LZTt7fG9DkTWL9tFVs372DyrI53bz2Joih49FY8jXWd7ru3uI7XNhTy3o6WuZNe/v4U5g3X7FYjEsysPObD5nARbul+kJ/BX1vD2dQEsZ1XZ9WkKLAHhM2DcPkofX0FD25Zx133/ZOU6NaTcrZF0zFNRRqWkIirtgkpdJitJ+NpfCHV07eTKUlTONZ4jHJbeceNT6HBUckn+5/kVysu5dz/jueSz27mXwc/52hTNZOikznmdPD9z66kvPGkkUwoCq6x8ZiO1LF0+fNBuwdrTBr2+pY61t//bw+LDe8SqRThGnE/W1e/QbTewQ79CBL0Hu4YovLFj8bz9V9u5PpF07osuHYvW01yQwWmK65Cr1PQW6MZd/1SZk5bS5btCqwVSQz03MzIK18OeEy9IYyxk59H6qFJWQJoVe5O5cCB9zCZa4iLv7pd33VNKB8FObhn4z38Gha9vnNfwfAIC8lR2ZQ3FlJVFng69Z7CaI1C527fy+xM3tlaxEVPfXNCSIQbdcSEGbh91gAevmTUCSEBMHVwIioKa/fltzVcp2hOiGhrCjxr76ksmHcdAJmLZvHr/3xAxMyRRB2y8fzPb2fZhnc6NdaB97Usy2GJidhLNLd0S9RJ1eW3ykYhhDALIbYIIXYLIfYLIR7yHx8vhNgkhNglhNgmhJjaSt9h/vPNPw1CiHv952KFECuFEDn+38FJD9mDNHs6Bap+cnoa+dEn53HOu3O5f9sLfFl+lCSzlduHzOHtBU+y+rpdPHvRSv467V7K3e4WwuKG638FQHVdSzVKV4lNy0T11tJQdfLL7Xb72NbQRAyNbE27iX1mKzNy/s6usBlcf+cfWPHnG7n/tosYmt25FVNrHP9sJR5Fx4TvnR7oZo5OY/BFjzLtxg0MWPhApx/SUQNmMDj87hOva7ct90fhahQdexWPx8KE8Xe0O055+W4MBjuRkT3r1SZ92v0pXfCwPm/xXEDlrVeW4PP2bcK4cGsEZjydiqd4Y6NWAOiuOYP48Mdnsf/hRex8YAG/u2AkN83IPq3t/HHarvKbg11Xb52KxaJFlNsaOyfcmhmQOgwVSV11BQajkTt++ihn3/cTDKrCvidf57F//Rinu/04nmYGT9RsYIXr1tBUVqfNL/ZkwkctjuJbIigAFzBPSjkOGA8sEkJMBx4FHpJSjgce8L8+DSnlYSnleH+bSYAd+MB/+jfAKinlEGCV/3W/ZljMMCIMEQGpn9xeJ3d+dhHf1JSxMHkI/5xxHxuu28hbl6/np2f9m9Epc0+sbBcM+0GrwqKuXjPiRUREB+0ekgYMACTH9p+soVFS0kiYcGASXpS6fMbs+D17jeMZde8HJKdnBu3aAGG7t3I8fShRMYEXjgmUzHPuYbT1r+iPmoleupVt8+ZTtmMHx4/vwGw+gtFwLkZj+6knCgu/BCAjo+P4ju7QVO1GVTyEWTsfeTxweDpjBk2j1lXKk397nvqarj30gkkgKSmaKa13YtIr/GrRcMZntL8+zE6OJVLnYffx4BiQw61aYKOtqWv/M5PRjNsssdfVnTg2ffr5/PiJ15DDElA2FPK3+65hy65VHY414S+PYvSpHNu/B0+9Nh9j1CmCQu0/NooOZyE1mvdpBv+P9P80O6JHAR3lSp4P5Eopm+sJXgK85v/7NeDSwKfdN+gUHROTJrK9vH0vD5/q5b7ll7Ctvpo7hszhbws/4Nyht2I2tJ2/vzVhUevfSURHBa+SWOpQbYVWln+yrGNVnZNYoX1QJ9nWUSNiGPTj91vEDHSXhvIq0mqO4xk7MajjnkrS1Ks4+9bdGM6/G1N9HSV3/JC9mx9HVRXGj7+3w/519VvxeMykp/fsjsJe50Uf5uuyeuvy7y1i3JAZ1LsreOOFJUGeXefobH0dj0/tVAnVwdE68hrEaTvErmKN1B5ZXVU9AfjC9HgaTu8fFRXHrx56lewbLsDQ4GXdX/7Jo/dfTU3RwTbHUXQ6ksIiKKqtomKP9kzRR52M7flWqZ4AhBA6IcQuoAJYKaXcDNwLPCaEOAb8HfhtB8NcC7x9yuskKWUpgP93q3kIhBB3+FVb2yor+75E4OSkyRQ0FFBpb30uqqry25VXsra6hOuzJnP3Wf8OeOwzhcWxCs2zJaaVgLSuEp2sCZ0VW46yfodmq6hudBPPySCkgmE/ICyIwqmZ3HXaTixmas8+hHV6PaN+8hMi//Y3zE1NxK3ais87gdjY7A77SvUwPu+goCaiO5XDuwsoOHIcX6ORiKSuR/YLIbjshoUMTBlFlb2IPZtbd3nuaRx2O24RuE/M9S9sosHpxeHxBVzBbUp2DDZpYE9u99VPEX5XVLut67VGZLgBYXO3eu6Ki+/kB0++iHtCLOTbePVXP2fdY3fjaqhttf05P7obBdh4eBXuxg8wRJ1M5fJtUz0hpfT51UfpwFQhxGjgTuA+KWUGcB/wUlv9hRBG4GKg00sfKeXzUsrJUsrJCQnBK73YVTqyU/xp9fdYVpbLxanD+fU5bf5L2uRUYfH5oU8BiI8OXiyFwax98PJUO7e+s4OC4w28s6HwxI4CYPSFdwXteqdSUay5A2YObT8nf7AYuHAhjgmS8DUKqSkXdNi+uvoIBmMjVmvPZGF956kv+fLZPD77x2EEChPmdOzV1RGXXX8+CgY+/Pw9dnyzv+MOQcbZVIfXEHhtig25mtHWp0oKqu0B9Zk/Vvu8rNxT2EHLjon0V8pz2AO7dmvozCaEu22bTGJMKrfPmcLS2cdR4urZsi2fl++6nt2v/QXVe3pm4+Szzua8S64GQPXmY3ef3Kl863YUzUgp64DVwCLgZmCp/9QSoIUx+xTOB3ZIKU91FyoXQqQA+H8Hz2LbgwyPHU64IZxtZS0FxT/W/Yglx/YwPyGTP85/p1N621NpFhZGr/Yh2fz6n1j1399xYPO7NNV1Pip0+Su/4KOnfqiN9ZmWsE7qrfiAy5/8hq9qG/i5XvPA2B82FWtE8O0HAML/5TRGBreEZlv4fD7Ij8CdJSmvXN9h+/wCrQpZWtqcoM6jrrqJN/66kqp9CqZ4N0q0jZghKqOmdD/zakRkOJdddBVSqnztr0DXm+jdTZisgX1eXJ7TVUfhxsB2bZOHpGEWXrYWdN/LyxodDap6ooBRV9CFmdG5298NRY+4nHi9k9en1HPeDWcTE6by5eff8OaPLqF4zfuntR3xve8zYtolAKfVllG/TXEUQogEwCOlrBNCWIBzgb+h2SRmowmOeUBOO8Ncx+lqJ4CP0YTNX/2/P+rs5PsCvaJnQuKEFjuK5zf/klfyvmF6TCJ/X/hBl4VEMwuG/QDPfBt765dTdaSOsj272cVu4HWMESqRyRZi01NIHjic9CHTSMqcgKLT3s5dq1+ivOAg+1cc1DLvq/4SoT+WFB88QJ0+kmGzkpniTOOFA8cZHWYhISwMmsA3uyMNYtdRXFrglLAEL4iwPY58+BFhdU0Uz09AVddhs1UTHq75ztts1ezd+wI1tRsBD0JYUdUy9HojWVntVyoMFFujgxVvbaVktwukQmS2l+t/fi56Q3DDl8ZMHsr2zcMpqNhPQ20TkTHBrz7XGvklVZjwEBUf2E6/eTcBYDXpSYwMzAamKAqDI+FIbfcz1SqKDkV2T1AYwyyoHtqNb9JHpfHgqB9y65GXqctK5ppnPyPnnb+yetk63nnmFc7ZupwpP3/+RFZmg79ei954Uh3Zn3YUgXxiU4DXhBA6tB3Iu1LKT4UQdcC/hBB6wAncASCESAVelFIu9r8OA84DfnjGuH8F3hVC3AYUAVcF4X56hclJk3ni+BNUOaqIt8Tz351/5KlDyxgfGcNTiz9Br2u/CE2gXHDOPVxwzj2oqkptxRGOHdlAee4Bqo4VU1/aSNXRfI58XQAsRzGoWBN0OBskJ3evp3/Iju75mJoCB8fNAxhuUfjVNeP5mXs0RqMep+NrjuQdZuyonjM0E6WtPBuLS0hOCU7unvao+t/bRBkMZF7zG4qr7mHLlsfIzl7IkSMvIpRt6HReIBIpzSjKMcxmOw7HaAyG4FRve+3Br5F2M/oYF+dcPYKRE4Jfu6GZUWOHUbBqP7s2H+KcRT2fsBJg7U7NUDt+xOAOWmp8sFOLm7h8Qhq/v7DtGu+tMTEjkn377BwuKmdYZufqXZ+JUQjsrq5He5vDrDiloMleT0R4dJvtLP4qllL1IHR6hl7//xhwYRXL//QT1m4txfbQ9zjn/pdQjCa8Hk0lpT9zR9FPUnh0KCiklHuAFkpbKeV6NJfXM4+XAItPeW0HWjwVpJTVaJ5Q3zqmJGv+z9vLt+O0HeJve95hWLiV5xZ/gkkf/MpfiqIQlzycuOThcEpxPJejnuKjGyk5uoOKglxqj1fhbjq5vU8eFc7oORdQtH8DOeuK+PiRFwCFvNiBzI3SVvVGf/1ssyWcoT0pJICBc2fiev5xDr/zIUOm9GxUcUV+PtEHDuI5eyZjx15I3vKnMRqXkJe/BKHo8fkmkp31fQYPPu/E7q+urhiLJXjhPKpLR1iqm1sfuDBoY7bFqIlD+OxLQUFeAefQO4IiNy8fn1SYOaZtQXGkrIGr/rOJ354/nAmZMXy8u5TyBicx4Z1bTM0bk8nr+w7xxa68bgsKa5iZmiY7UlURXdj5W6yROIGauop2BYXXX/5WeE7uXgyR8Vz4l7f46k+3s/1ABfk/uozZV12Dz6PNw2A6+UjuT8bsUAqPLjAibgQWvYVX9zzLodqjZJnNvHjBR4Sbont1HiZLFIPGLGLQmEUnjlWXH0JvMBEVO+DEsXHn3MSKsF+y7/ODkAQFlkwy44Pv1dQRAyeM4JMhE0hdsZSqe+8gPr17X/j2yH/9dSJ9PlJvuw2AaVOfZtu2RwgLy2bs2NuJjGx57ejolhl1u4NEoutk5HVXCQu3YBYRVNZ2LmtAd3DWloElFmM7qrQvD1VQ7/Dwm6V7+fEcbUc1LLnzNqqZo7IxsJ9NedXc3XHzdklOSaUqv5Di3BwyhgSWeflUwq2R1AK19RVkpbWdgiUr82zY+Sg7i9bgsFcxffxtREVnI3Q65j3wEpnv/YN1n6zgg1dP+vgYWqie+oeNon/M4luGQTEwzBrDvtpckkwGXrrgPaIsPffQ6wxxScNPExLNLLz5Ma5/9EHcc64BIRiZ3n2Pm64w/Pe/xuR1880Df+3R68gtW7DFxhA7Rdv9xcYOZMGCFzn77P/XqpD4v4DFHI7LHbwEeu3R5HARptqJTWzfI29g/MkAsqdX56JTBD9f0PmHs0GvY4DVx8Gq7hcyGjFuPAB7t2zuUn9rhLbrrG+obrddTPQARqo6XrTl8IvCD3n2i5MiTgjBkKt+zs3/eY+5s/0VLIUJk7l/2ihCgqKTlB6tY/V/DzNOKWSgycez814iwZrd19MKiJSsKRyqcBFnqSchqnfKZ57JkKnjyJ06n4Ebv+Do9n09dh1dfQPe+PgezdfUMRJk713foDfik8GtCNcWxyo0D6ToqNaLP3l9Km9tKuCut04vG/r7C0YQbuqaImNCmpUar5FjlXVd6t/M0FGjQUoKCrqWPyryhKDo2Avr4ZkPM1/R/ke59rIW53WWSCbe9RjTJ12EOeoWDKc4emiR2f1DUIRUTwEgVUnhvmp2rCikNLcevUnH4JE/YsKQf2NoEm2ECvZPDlWaGRwXWC6anmLGH39L8flrKPj7vxn89n965BqqXg+e3nlo9heio2KoaCzkWF4pGQODF3vTGuXVWoBmzBmC4uaXt2DSK3x1qAKvetKF9OfnDeVHswdiCLBAU2ucMzKNdw7ns2L7UX7QDYO9wWAgwmigpqkB1edrtzBYa0T5g1FtAWTNHTb0Yp4YejG/eHMWhzwtK+uVVhZhdzupUj3oFf1p/if9qWZ2/5hFPyZvZyX/+9MWPntmD021LmZdM5RbHzub8XO0coU1le15BfcvCiuLKLfFMCkz+Ab3zpCYmULx8IlEHem5HYVMTMBQGbyCN53leH4FwmfEZO29r9icBZpb76pl63r8WlV1WoBmXPRJe0NZvZM1Ryr54kD5aUIiyqLnx3MHdUtIAMwdNwg9PjYc7X6GhsyMdLxGC0WHD3S6b6x/N25vCjz/VJwhgkJFcqz60GnHb9y2m5lH7Pxw2tlsz7LiajqpOgypnr4FuB1evnzlAMv+sxeAc28ZyQ1/nM7YuekYjDoSU7UKaU0N3auf25t8vW8nALOHt13drbcwhIejBCF3T1voBg0mrKkJR03fpOJe9to2QDLrkp6raXEmqVmJxJhTKSrP6fGsslKL0EF3iteQx59B1qhXCDdpQkEA4UZ9t+OKAMLMJjIsXvZVdL+639hJk0EIdm/Z0um+zYLCYQs8X9TFo28G4KEVPzqZukRK9ptP1kr/eJqV7G2HSVm1k4yVO/nTAivrLX2bHbiZkKBohdKjdfzvT1s4sqWMKRdkc83vpjBsWjK6U7aBYREx+FyROJzdTyvQW2zIrSRM72DSoLF9PRV8egN66e24YRcJy9bSPtQc7v0cSJXl1TjLDMQMVEjL7l3D+cCBg1AVD8cLezbRgd6vrnF7Tr6HVr+rtapKJvizwkogPiI4cSkAY1PCqHDrqazveq4mgMHDRyCkSmFR57+/YaZwPDoVlz1wQTFq1DX8zjqSzZ5qPt2gOXIcKzn9s3ljXS3Xu01c4TFxvseI16hQlxncxJxdJWSjOAWfT2XbZwVsX1ZARJyZy385ieSBbacnkJ4UvPJYp65RsX0d3gonqeef193pdgq318uGwigmp9ee+JL3JT6dDoPagzuKyCgk4OtGltCusuPrHAQ6xs9t6X3W00T601Q31nbvQdoRcVGayqm67qTe3WLQPldeVbL+aBXhRh1CCP5w8aigXXfWsGQ+yjvOih1HuXHuuC6Po9PpiAmzUFffgNfjQW/oXIJGrxGkvXO2vqsueIEP/zuLpw//lwum/4p79hxAZ8rijSQXc4ZPa2ErGbZuL4awrieODCahHYUfj8vHB3/fwbbPCxg2I4Vr/t/UdoUEgF6kgyHw3EuF77+Hewmoa8w0HuldldWXezfT5AnjgtH9wzXUp+jR96CgEH59+JlJ2HqDgl21SIOLEZN6J/nhqegUbe3n68H/LcDIAakAHC87aS94fl0e6dEWHrxIi7qeOTiefQ8tZGJm8IIYz504GAWV9Ye7Hy8ycMAAVKOJnF07Om58BqpRwefonCuyzhzJ/LhxHNfBqxuWssE8kEctJcwbdVarBnWdgP6heAoJihPsWFFIeX4D5902kvk3jcBo7nizZTJloTfX4bC19GY4k+LPP0a3NQlPuPbFqv5yV3en3Ck+3H4Qs87JhZPmnDj25Os7Oe//raCqvPdX3camepo6KCLUHYRee/9kLwsKu82Bp85IdJYuKHr5zqIo2gPHp/bsfcdHWXFioL72ZPrsf63KobjOwV8+1wy2EQF8hzpLdISVVJObvWVdz9XUzPjpMwDYu6P9+jKtIU06VGfrqcbbIzkyA1WJ4i/OZM5y5nH9tLazGuuEQO1ssY8eIiQogIZqBztXFjFkShJDpyQH3C8iUgtaqyw90m47n8eDd5MeV2Ixmb+9CNeYI+iLEqjatjngnPzdodFex/qCKM7KaiDcorkzfrkmn38cKCHH6+WrNQU9PoczCasqoyqi56LDTwgKT+8KirxDxxAIMob0jc+04q8N4ZM9vxb1Ga24Gk8GnTV/lt0+FYNO8LsLRvTIdcckmSlx6qm3d8+onZaRiU6qHCvpqOZaS4TJAM7AP1s7qovJ/vJL7rZn0xh7M07FxGNjR7ebQkQvBN6QoOg/bFyaiwBmXNa5pG1xSdoXobJkT7vtGvMPoHdHYJkYjU5vIPG8c1B1DpzvuSn424ccW/4hjpqWwTjB4vV1K7F7w7hu6kgO7C7no48O8Zvlh0j3rz4PlganzGRnMFeX407oOTWY4hcUqrd3YylqyrTdWVxK64FoPU1zFThdL7hVpg8YQrivic0H8rG7vKgShiZZmTMsgQ/umklsePCM2KcyY3AiEsFXu7qnvhVCkBAZQZMKrk5mk1UsJoQ7cGG85NgenLp4nGFTcIdN4UfefQxqJ/0HaA9nX/+QEyFBUZJTx9HtFUxYmEVEbOc8DFIzx+Bzh1NX34GLncH/pfXXl4hIHELifePxzTqOVH2I1XFUPXqY/H+9S9mGL6kr2IcvSMFiqqryxhZti/y7d2pY/PY27tmYS5NUeWzxSAYbDByu690APKfDSUxTLca0tB67huI3Ttobe7eedElOHRKVzD7YUaiqyt69uwGIjev57LxXzJ+BKgUr1mxkW6GmghqfEc2rt0xldFrP1DQBOHfCYEDyzeHO7wTOZOjQoUidnr1bNnaqn8HScU2KUxliPblwCPeU8cu5N3TYRy8Evn6yo/hOez1JVbJ+SQ7WGBMTFmR2ur+i04FrFF5d+4FjEZkjaNB9jfv4SZ9+c3wiWRdci1wsacg/SM2WnYhDEXg/NtFELTVRH5H6o9mYY7pX1e/Xb62kzBYNQIpRz30T0hkyKJbhA2OwhhsZtrGALTU96yFzKnUNDlZddTMjgbgBGR227yphERE4AXzdr2EQKKqqUpXnQhcpe60mxKl8+fEGyhrzyY4bRdag1B6/XnpiDO7IVGRZDu+u16531uCeTzaZmhBDnN7DruLuqxUnnnU2a7fv5MDevUyeE3gya0NYGIpHoKpqQLaoZwpLQRnCg+kObhwwF6O+492Wrh8Jiu/0juLgxlIqixqZcfkgDAFW2zoTs2koOks5Hlfbq3KdzoBU1Far0AshiBo4kgHX3kDG7xZgvt4As+vQN8ZSumRVl+Z0KgP4CoB/nzOADx6cz3WXjmDymCSs/jTPwxOsVEqVqsqeFxYOt5dl37+bkYV7Obbgcqb/4Loeu5YU2kdb9LD3z6kc2FqAcJvIGhfda9dsxudT2bZzEyZh5aa7rui1695+3WV40SELtqIguXBMz6YOaWZCsok8m47S6o4dSdojOi4Ok1Qp7WQUvzk8HEUKGm11AbUvVoYAcGP2BCICEBIQ8nrqF0gp2fppPskDIxkyueu68oiIgQghqSw92m47oSqIDvTGOoOZ+LHTST//InyjS9HnpdCQd7DLc2uqPMqw1Pf5bPZGLlo8stWVz4iMaAD2H+nZdBc+VfL+Hb9m/IFvqLrqZhY8+Wf0ET236rZXad5lppjguWZ2xJpXCgCYem7vR76vXb4Ft2hi8vjpvZofaFBqPNkTzyZBsTHJVIa+l65946zhqCi8sLzzHktnkhwfi0MoNNbVdtzYjyW8OY6kc7bFCEPg6u3QjqIf0FjtpKnWxbDpKd3KMBodpxmkqiva9nyq2rkJxWdBiQi8WEvKBeeh6p1Uvb8bj7Nrq6aCnc+DVMie8IM224wequmyDxTUdekagSCl5J1fPMKkTZ9TOvdCzn741z12rWZseVpm0IjBgVVfCwZZU7Rgt8M7OxeE2V2klGzethG9tDD3ghm9em2AOy6eTZ0llZEU8/aXnU+J0RVmjx1AssnLRwfq8HbTBXrosOEgFHZ3Iu14c6rx6rqOI+CfOaLZP3Rq5+xlOkFIUPQ1ZfnawzdpQPe8UxJTNUHRWN/6jsJVW4ftg1rcERWkLlgY8LjmqERMi4wYq9Mo/O/STs/Lbaujks+Jds0kPDm7zXZJ6ZHEIDhU0nOeTx888h8mfP4mxeNnMvfpv/VK6m9H7lGkEMQO63ztg66y4MaJSJ2HfeuO99o1AbZ/sw+nrGfs8Cnou5l4rysIIfjj3Tfh1FvZs+4LduT0/P0LIbh+cgrVPhPvrN7VrbHGTp0GUpJ/NPAEn1ERsQDU1LcvKC7+5iMePm4B6SZZdC7vmE4IvP1DTnQsKIQQZiHEFiHEbiHEfiHEQ/7j44UQm4QQu4QQ24QQU9voHy2EeE8IcUgIcVAIMaMz/XuKsrwG9EaFuNTwjhu3Q3hkHD5XBA5nQavnS99ehfAaiLo6HUNY5yp7JZ89F8/oYsxHBuM43rncPUVbX0fVO8ga0vZuAvwFVMJMHK7vfgBTayx/6i2Gvvlvjg0ay7xXn+5S6cmu4Ms5ij02BpO194zKRpOB+MEGPNUmSno419Kp7Ni6GyEVzr3orF675plEhJm57abrUYTkv+8u6bhDELh9wQQsipdXNxZ1a5yI6Bj0qo+q6vYLEZ1Kc2LAuoa2VbbXb/qELe4sRugKOTJrLNvnXdKpeen4dqmeXMA8KeU4YDywSAgxHXgUeEhKOR54wP+6Nf4FLJdSDgfGAc1K90D79wjlefUkZkUGRZ8rPQl41Zapj+sLDqIrisc3toy4IdO6NHb0HG1FXLM18DQDqtdLqe1dLM4hxA+d2WH7sckR5Hi91FYHV1isef5t0p9+hJLUQZzz9ovozD3jV38mUkpMJSX4Mns/hcbMi0YhEKz7pP3YmmDh8/korysixpxCmLVvE8gNzUolLG0oYe46PD2cvRbAYjJw/hArOTYjmw90L6Yi3GjA1ongzKgITWXrsLeuTvqg+DBf2VPRq3aWz7yAyE7YJpr5VqmepEZzjgeD/0f6f5r1NlFAC6dmIUQkcA7wkn8st5Syrnnojvr3FF63j6pjTSQPDE5QlEI8Upy+spBSUr10L6rBQcrCBV0eOzptAs6YQjyHAo9CLdn+KW5LKenJ3wuo/QWT0/EBL3/UdcP5max8/EUS//EwxckDmLHkDcyRna+T3FVq8/Ox2O0YR/S+UTljcBL6SDcVh914eqFw0pG9+fiEmyFdqP3cE2jPNYHSS5UF77twMgqSJ5d3r7ZJhDUcL0rAmRLCwrVnh6eVfE8+1cdPD5Wgky5eGB6DSddxFMIP1/2LMa+NYcxrYxj92jhGvzaWgn1XUlb8fOdupIcIaDkthNAJIXYBFcBKKeVm4F7gMSHEMeDvwG9b6ToQqAReEULsFEK8KIRo1vUE0h8hxB1+1dS2ysruFywBqDzWhKpKkgYEJyhIr09EMdac9iE79t77GCtSYFID5piuB18JIdAN9aGvi8VRHpg643j5G+g9MaSNuzKg9hMmpnJOuIVXj5RTF4Tgu5y9R4l95SkKMoYz66N3iIjtueCr1qjYrBkloydO7NXrNjNsRhKKx8y2tZ0vitNZcg9phvOREzqXVaCncNhtuIXhtJT8PUlGQhQzkhU2VQgKyrpeeyQsLAwUBXuANSaiIjUbhcve0q18e00RHl0MV8baOT+t4zQmP934LBvyXgQgLeE8Rmdcw+iMa4kwJxOj9q5jRFsE9G5KKX1+FVE6MFUIMRq4E7hPSpkB3Id/13AGemAi8KyUcgJgA37jPxdIf6SUz0spJ0spJyckdC/4rJmyvOAYspsxm5LRGW3YG5tQvSoFb7+Dsj0J96ACMi66vNvjx0zS0inXbN3WYdu6vL00WXeRZL4cnS5wVc+9i4fTiOSdT9vPW9URzoZGvFddRJjXxYR/P054ZPdsQF2hcbem9kmePr3Xrw0wfcEopPCxb21xj1+rsqISpCAtq39kBXbbm/Dpe1cF9rPFY/Eh+PvHW7s8hsVfq7qpPjAPw/CwSFQkLkdLde3OOs3WMSbAXfRXOS9hCB/Dmms3s3zxP/jfvPv537z7mZowAh3dL9IUDDol9v1qo9XAIuBmoNkdZwnQmjG6GCj270AA3kMTHATYv0coz68nIs5MeFRwdOZh4VqQUVXeHor+/SH63am4hxaRfet1KAFsOzsiKmMcrqjjuA92vNovPPwiQtWTPf7WTl1j4qRUBuh0fJHbvV3by69vAkDVGUgePrBbY3UVb14u9ogILPE9HyXcGuZwIzGDBO5yC1tW9eyuoq6+DoNiRq/v+yQLbo8Xo6uOsOjgLOgCZdLQDEZHevgi30VNY9d2xHqD5rrudpzeX/X5cLu1XYO98aR6WQiB1wBeR8vr5ft3GcOtgcbwKKRHDiLWdHo2ZYvBgsPTt/XtmwnE6ylBCBHt/9sCnAscQrMpzPY3mwe08C2TUpYBx4QQzQrU+UDzN6fD/j1BVXEjhftrSB0cHbQxI6LSiCqeje5/KkplFJ45+Qy4JThCArQPpX6YwFCdSF3u7jbbuWw1VOtXEuOegzkq8Cy4zcxIimSfw4XP0zVD5MYNRTxe7KY+LBZTat+VW1Vq6/BG9a6660wuvn060uxgy/vF7N7QfjBmV7HbnDR4y4mL6Px73RNs2p+HQagMGND7TgQ/OXc4bqnjHx8FHgtxKs0WlVMtFMdz1/HFpxNZs3o8Kz4dx8at01j9yU0nzvsM4HW0XPHXuLWHe0Z4YPm2FH0kTa6WwX5mnRmH91siKIAU4GshxB5gK5qN4lPgduBxIcRu4BHgDgAhRKoQ4vNT+t8NvOXvP97flrb69yS2OhefPb0Hc5i+05li2yMmYSjJB24BIOL6OAYsugkhgqujTTl3AT6DjZr3D+F1tK5HPbbzTaTORebgzu0mmhmWGokLKMiv63Tf/QcruePjfaTodMQMHou3shCp9o3Hhq6xEdnHgiIiysrFd09E6j2se72QT17egKoGL++Uz+vjrRffQwofU6ZOCtq43WHnQU0gnjW29w3rC6cMJ9vi4oMD9TjcnXci8Li1xJnmMG1VX1+Vz75DtyP0Ttz1EejDmlB9Aq/lG6rKNMP5sDnHSc1suQgodbpAqlj0Hddb2VJViPSUI2XLz4ZFb/n2CAop5R4p5QQp5Vgp5Wgp5cP+4+ullJOklOOklNOklNv9x0uklItP6b/Lb2MYK6W8VEpZ217/nsLj8vHZM3tw2b1c8OOxhEcHz1UzKjYDn/DRFKMSM3p80MY9FWNkDKYFRgw1yRx7++MW51VVpazhfSy2wcQO6ZoWb3i2tlU+lNs5o2BZlY2b39iGScAbt00jcvRIpLMBV27vBp41Y3DYkVF9k+b7VDIHpXDDAzMxxDko2uLklYe/wBuE+hh2m5N//vUZjtceJT1mGJPOHh2E2Xaf0uPFODAxNKP37SVCCH5wViY2Vc+zn3f+UWL32xoio6Oprchl46bF6EweRgz5Nwsv3cS4kR8ybsR7AOzb9heO520gNqORzIGVLTyljrk8IBRiDB1rFB7a/gJSGPjz9HtbnLPoLTh9zl6pWdMR35nI7EP+BIALbhtFfHpwXTUVRcEh9KiOwFN0dIXkWfPwDC1CdzQZ3xnVtaqOrMFlLiYl+souRz4P86fzOFhUx45dpXz0xVEaattf0fi8Kvc8u4lGVeWlS8cyYGAMlvFajWT7lr1dmkd3Ubw+MPZOzEZHxMZH84OHzydhNDjLjLz39Jpuj/nhf1fQ5K1m0rDZ3Hr3NUGYZfdRVRWaqlB6sBhVR1w3bwKJeidvbi/H28mswbYmG0L1YTSb2bL+TvRmN8kRvyBz6AL0BiPxyWNITB+Pp3YAHsMW9u770Ym+FbbTnRaMhmgA1lZ0rHKssFdjMsRxVmLL2hRhhjBUqeJWO19JL9h8ZwRFdYkNU5ierDE9k6ffZ9Fj6ETFq65iGReLohqo3PbNaceLc99A8YaRPqnrGVmjI83ogKfyK7j8fzu456vDXPTYGl7872727i1voTqxNbm567G1bLY5+c3odMZNSwcgfJrmpeXY1/Puoa2hqCrC2D+K0gPodDqu+vFcwtLcVB2CnL2F3Rqv6Hge4boYLrpubp+UW22N/QUlmHGTmpbeZ3PQ6XTcMCmRGo+et9Z0Lq7CFLWTESNWs+LDaeijczE6z2P0tDtbtJs4/TF8LgMGqw2vW1Nv5lacLvxnRWjPgY21dR1eV5VudErrixqLXvPE6g8G7f7xKesF6spsxCSH9VyeoWgTJinxuXtWWCSMnY/XUo/96xqc9VqBeXtlKXWmb4hjAYaw7qWsmB+lubP+eUo2fzt7EKqAP+0p5qK3tjHhd8v50V/XsOSDA2xYW8Dlf1vNF/U27hmewvdvGHtiDENSHCI8DteRQ92aS1eQPh+KlAhD/xEUoKlGLvnhDBA+vn676wK0qrQOp6wnLaX3DcZt4fOpvPHuh3ilYL5/kdBX/HDxNCIVF8+vy++UyiYpcw9xSSUInZtw39WcvfiZ1ttlTGD6jE+xqjcwbMJTAJTVnO62vq5Wi9ZOMHasYZCqC6UjQdEP7BR971PXS9SW28kcGdtj4xsTLCjHm2jIbyRmWM+lttYbzERflUXjG1WUvPoV2T+5mqKdryD1XrJG3NLt8f91z1lICWH+ehVXLR5KYV4d63eUsCW3mhV1TSzfrBnTw4Cn5w1j8YKWGVqNGcNwF/T+jkL1GyX7m6AAiE2MImm0kYq9Kgd35zFiXOfdh7dt2A0CRo/rO6+yU9lxpIh3P1pGmLOKhFEzGDmg5wsmtYfZZOSy4RG8dsDNp1tzuGhq++VGAWpqCjAYnCjKZSy8+LEOF5PRCQOZdu7DeLwuDkuw23NPO1+uRoMOrkjv+D1SfU3oDK3b08w6LR4lJCh6CbfDi73eTXRSx14IXSUsPQK5q5LGgvoeFRQA0SPH0DTnQ4xfp5P/1huUJ7yP1TueqLTuGzUtYaevghRFYcDgWAYMjuV7QJPNTW5+LUfya5kyJons7Nbv1TJ+Aq5DG3AcyMcyckC35xUozSVkha73s6gGwnnXTOLNvZtZ+7/DDBud3elcYweO7EWvWhg9seMHYE+SU1zOK0s+RV93DB0K5qyx3HnleX06p2buu2wG7x78kidXHgxIUOTkaEkMszIv6ZTGYX3Os+gFxEVPOe24Dk1FG2Vof0ehqipedxlRka17ifWnHcV3QvVUW655NMQk91yUcOQgTV/pLO2dsqLpCy/FO6IU04FBWGqGkZXVfpbYYGENNzJudBJXXTS8TSEBYD1Xq4vQsOIbPKXlvTI30NQgQL/R3Z9JdHwEaVN0eGtNfPifztVpPlZYQoOrioyEob1anOhM7C4P/3n5DZS6YvTJQ7nzx3fzm1sv7zf/8+gIKxcMCSOnUc/Xuzo2KNfWrsbliiArq3PZdz0+7QGeGX96BgCzogmbGnfLPFCn8rNvHkGoNiYmjm31vMUQEhS9Sp1fUPTkjiI8KQyPlHireu9NTb/2UlTFTUTZNEqPfUTJ9o8o3vwe+d+8RM66f1Cw+RWq9m3C6+z9D1r4tLFgDKPmP3/m6Nw5FP+yd5IDN7s51geYiqEvuPj752BIbqJ0j4u1H7cdQHkmXy/7BqRg3vl9l04c4O7nPseqNhE3ciYP3Hk96Ym9V0UwUH59+VkY8fGPZe173jmddej0RxBiIrpO7kLNxmgA7K7T05PbpKYyyre1n7b8SI2WhPO8tNbjYEI7il5mzduHAYhKsPTYNRRFwalTEI2958pWvXcDimrEE11KrW41B+t/xmHbr8lzPUKR52lybX9id8UNrF03kQPLH0INgg9/oCgGA4m/ehDLpPPQJQ6i8dPXsW3reXdZr1/15GgltUJ/QafTceMvz4VwO3uWVbJ/W26HfdxuDwWlR4g2JpMxuG/zOqVZtMj9Gxf1TS6tQEiItrIwW8cltnc4sH19m+0OHXofRVFJS1vcZpu2sBj9qcbdp8cduRTN5jA9Prvd/teMuh2AA/WlrY8fEhS9i8epfbB1+p69XV+YHoOr98qh29aV4jXXM+qmPzBj8hrGZb3OhMHvMG30cmaO38zkIR8zNO7PhLtGUGp8ncaS4KURD4S4Gy8m+60nyXr9BYTBQvmfH+/xa0ZEal/S5OT+kdaiLcLCw7j6F2eBzsvXr+XgbCVd9ansWL8fVXiYMHFCL82wbYx4UKUgLqLndujdRkpu0i3nB/plxC5v6ebaTFnZCjweEyOGX9jpS4SbtJgRp6fWf0nJgTqtWkKk7Lj+9sjoDADeP9oygBZCgqLXmXxBNgBqD6eUEFF+F9leiKeozdmFsTwdZYILncGMJSaJ+EEzic2cjDVxCObYeKIyRpEx7lqizFoexvCkvknSZ8pOwTh4HJ7jBT1/rYgIfIqCr7bjL2pfk5ASy+SL0xEeI1+8t6ndtjVVdQAMHJbRCzNrH73egEDSYG9fuPU2ufu2suXVX7P7qyXk/HUmU46/DkCyp4iyXStbtPd4nAhlL1KOxtCFwkJWk5b80O2pY2/tMQZ9+TnzdmqlAO5K7zgzwGi/oCiv+pJye12L8yFB0cuYLJpzl6eHH+CGRC1Oo76g5/XjtV/tR9U5SJ4/v8O2dm8BBmc8enPvp/xuRgm3It09/4HX6fU4IyJQS8t6/FrBYOq5oxAmD0U7GtvNBeX1aTtVo6nv3X6z0lMQArYeyO/rqZxg04v3Mei9c5la8Bzj1v6ABFchKzPvofEnB3BhwrXyjy36HD78EXq9m6SkRV26ZoTFXw7V2cDiHYXY9Wknzn0/e1SH/S16I4pRcydOMLcULCFB0csY/YLC5ehZQRGeoaUGaSxovTxisGgqK8RQkIY6vBqjNbrD9m5ZicHbu6mfz0SxWJCe3smt742NQalqu5Zxf0JRBKZUO8IRzo61h9ts5/OXFjWY+t7td/bEEahSsGln36RoOZP1bzzE9OKXqSCWnbOeZ33Mpdi/9wXn3fowEfFplA+8gizbbkp3nr6rKD7+CT6fnpEjrurSdSOMcfgk7LV58ShWLrCWsWpCMmsmpRFtDGyHoihmIiMnteox1hxH4fT2/c7tuyEozJqgcDt61n4QNSgaAOfxwKpkdZXKLzcAkHBeYN4vXuowaJni+wwlKgp8Lry1DT1/scQkjP3Y6+lUtm3bxjH7XqTBxb4VVbjbWMx4mwVFP0hNkhofjSssAUdJDoeLes/1uTU2fPQCZ+f+g53Wc4j69V4mzL+Gs+95jdRBJ1f0yZc+jBMT7pUPnzjm9bqBnXi9ozCbu5b7Ta/Tky/TkP7HaILRzKjoZIZFBr4oE0KPT219AaVTdJh0ptCOordoVj25e1j1FJ5owS0lvuqee2NdDbXoDsbjySohPDmwNA5epQG9Et1jcwqEsIljAGhYsaHHr6VPTcXkdOLox3YKKSW7d+/ms88+Y8jQQVz202nY6zwnPPTOxOdrFhT9I0b2e1dejILkxdffIq8kOCWKO8vetR8wacdvOWwczZi738FkaT19jTEygcpBV5Jl38Px7csAyMn5DIPBSVJi19ROAHafyoO6J3ld9xMAypydWyDWuprwOI4gWkkx3oxFb8HubVlFr7fpH5+6HsZg0bbrba3WgoUQApdeQTR1Ph9+oGxfsZ5MXzSx88cE1N7n8+DTN2IUfZfVEyDqgtmUPxJG1dNPgpBIpxtfYxPS5SL6sgWYBgYvmZw5KxOAmsOHSeujcqjtUVxczEcffURlZSVpaWlcddVVGI1Gplw4gC2f5JM5Ko5h00732lJVv+feKb7+a9asYffu3cTFxTFt2jQGD26ZSqWnGD0onZFnnceBDSt58fnnUNGhl15c+nBiUjL52U2XYu7B3U/u/q0MXXU7xfp0kn/4PnpT+x5YyZc+jPPx9/B++WfkxEUcO/YRCB0juqh2Agg7I+jxRwM7l1Ylt1GLs0iPart+R3+pSfGdEBQndhQ9LCgAfOEGzA09E0vhcnsx74thS6zC5UMCExTOuhIQEpOpb33vdZFWEn/+ABWPPUD5gz877Vz9R0sZuvbToF3LmpWNA2gqKIA+FhSFhYXk5OTg8Xjwer14PB4OHjxIWFgYl112GaNGjTpRxnTS+dkcO1jD2rcPkz485rRSvV6vF+RJQZGfn8/XX39NcnIy5eXlvPnmm0yZMoX58+djNvdOzerrF85ge1YKSz79Ap1ej95owl1fjbt4Pw88XsaffnEnxgBqMnSF0nWvk4WPmDs+JiquY1doQ0Q85UOuISvndY5t+wxVbkf1DicsrHvBggOVUvJUrRTy9ITOCeqRUVo/k6Ft1VdIUPQixhOqp56PcdDFmDE3uPE0uTFYg1ufYsP6Ioa4JH8cZWSuo5IYS8e6UNtxLZjLEtf3bpVx378E66yJ2PfmoI+MQImyUv3869jWfIT7eBXGtODseqIGDcQB2IqKgjJeV8nPz+e1115DCIHRaESv16PX60lPT+eyyy4jMvJ0TxdFEcz73gjefmgzO1cWcfaVQ06cK6o6dKJep91u59NPPyU6OprbbrsNgFWrVrFp0ya2b99OVFQUl112GZmZmT1+j5OGZzNp+OnFKZ96ZzlVBzfx5rJ13Hrx3KBf89ih7UwsfYeDlomMSQr8HpMu/gOOx9/B9dUfMEyxExG7sNtziVY80MXChWEGI1KYsHvaTvtj1vePcqgdCgohhBlYC5j87d+TUj4ohBgPPAeYAS9wl5RySyv9o4EXgdFoJWlvlVJu9J+7G/iJv/9nUspfBeGeWmDsxR2FKTkcChuoy60nYVzwPI1UVcW0qYyjVoWN8TqKnW5iAgg0r6vcAQJiMjtfLtOZX4+7qJGIc9KClp7dNCgD06CTQstz4SJsaz6k4fOviL/96qBcIyo7m1LAXVISlPG6SmWlprv/yU9+QlxcYHVQopPCGDQxgYPflDL1wgEnHDG8qqbOfOaZZ2hsbMTlcnHDDTdg8GfJXbRoEWPGjGHfvn0cOnSI119/nTvvvDPg6waTO69cwP1/2sXhQ4cgiILC5Xax+fmfck7V/0CA6YK/dKq/ISKOkthpZNZ9Q4E3lpEjr+32nCJ1KnhhjL4QrdJz5xDCgMvXtleTRW/51ng9uYB5UspxaP+JRUKI6cCjwENSyvHAA/7XrfEvYLmUcjgwDjgIIISYC1wCjJVSjgL+3o37aBe9QUEoosfdYwHCM7VtZFNhcL17duwsJbPBx+rBKghBlDGwmIgGx04MriQskSkBX6uuYB+Hn3iGqv/soWFZPuWPb8db1zOurRHzZyCMETSt7X7lt2YUoxFXWBiyj11kExM1P/uams6Vlh07PwO3w8uhjVosiK3eRWL5OYzKmE54eDjDhw/njjvuYMiQIaf1S0tLY+HChdx88814vV4OHuzdSPxmdDoFxRoHjuB6nu1458+cU/U/7NLE+oRrGTxycqfHEMPPxaj6sNYJrOHdF6JWHURTx8pZl3Spv1BMuHxtf7f6i+opkJrZUkrZbM43+H+k/6d57xwFtFi+CSEigXOAl/xjuaWUdf7TdwJ/lVK6/Ocqun4b7SOEwGjR9cqOImpwNBD8LLKN645TZRJ4B2kGsDhTxwWKVJ9Kk/4gESLw9OOq18ue/XdQMei/J455qxyUPbaVpi2t56TpDrowI8JiRW0MrmeHz2hAuHonbqMtUlI04Xz8eOdqhycPiCJpQCR7vj6GVCU7VxYhJcw/fxa33HILl112Gampbdd9iI6OJj4+nsLC7lXS6w56gxEhg6fq9fl8DM59jT3mKYT9oZyzf/yfLmXQDR+qCZeYIHlARugFdmlBtuO51B6KYsLdjiD41ggKACGETgixC6gAVkopNwP3Ao8JIY6h7QZ+20rXgUAl8IoQYqcQ4kUhRPNSeCgwSwixWQixRggxpZX+CCHuEEJsE0Jsa97KdwWTRd/j7rEA5igTLglqTfC2i7l5NQwpc1E8PpZyKYikgXB9x+alptIcfMZ6oiInBnytI18/hsdSTqRuAl5rDa7EQqIuGACqpG7pUarfPIAzt+5Ee0+5jep3D+Mq7nqQoWqvR4nqflGp4kN5rH/nc9xuDzq3hyafSkWTDVVV2XO8FF87kc89gclkIjY2loqKzq+Bxs3PoL7CwSu/+YbdXx5j1DlpxKYGHlmflZVFYWEhbnff1FtWFAWF4KXM2fHV+yRQh3fk5dANNWhc1ll49IJ4GRy1cJReh1uYcHi6FjulUyx4OlA99QdBEZAxW0rpA8b77Q0fCCFGA3cA90kp3xdCXI22azi3lfEnAndLKTcLIf4F/Ab4vf9cDDAdmAK8K4QYKM+oXyilfB54HmDy5Mld/uQZLfoeD7hrxm1UELbgucjmryogQwdT5w3gmT1HSRJ1AfVrqNTqBkfGtV2e0udx05C3n/2592JQY2gK06Jth896kLKGrzHuysA41kTSyMmU/2M7jn3VOPZVI8w6EALp36U5dleS+rtpKGGdd4kUOgPS0b0dWFNVLSXXXE2cy8b6J5JJsdup0ut57OlnkNFxWEuLeEfR4YiM5sc33sCg+J6rdngqRqNR81jqJIMnJeKyezn4TQkZI2KYdc2QjjudwtixY9m+fTsffPABV111VZ/UihCdKEXaHlJKBq+/j0YsjJj/vW5OSuAxm9G5grODjdRpj9BaVwNhxo7zO52JXmc+UdeiNfqLoOjUp8evNloNLAJuBpb6Ty0BprbSpRgo9u9AAN5DExzN55b6VVtb0HwHeszZ32jW94rqCUC1GjF5grN6raqyMTjPxsFhEYRZJPu9iYwIC2xsZ1MxAOHxpycDtNvLeP+l77Hi8/GsXjeCHcevxGUupilsLzqPlWzTzzFFxGMdlYVAoWbHDgxxFpJ+NgnrrDT0CRakT4JXRRdjwjggCnyS6re7ViPbOGAUrqN7O1XjGOD40SI+vfdBVj3/Dhv/9SIRLhsHZpyPXehQhaA4Mwuj24W1VPN+Mqg+Iuuqef7559mY3ztqGSFEp++rud/oc9K46rdTOO+WUeg6qWbJyspi4cKFHDx4kGXLlnVpDt3B0ViHWx+c7LLOugpiRBOHrNOwhHctivpUfHoDijc4aslIfxW7WlfXdtQGnQWf2v+N2YF4PSUAHillnRDCgrZr+BuaTWI2muCYB+Sc2VdKWSaEOCaEGCalPAzMB5oLKX/o77daCDEUMAI9Zn00hxuoq+idCEddrBlTrRN7jYOw2O7VwNizMo+BwPB52Xx8bBcuwrg0KTB56nSUIIQBY1Q8u/97iD2byhgwJJrd+2uAW4jM2kjm+A/wmmoZZP1/ZEy6ASH0J1af0SMmUBf1Pvo1kXinOTDEWYi+YCBccLrgkVJS8vAmXDl1+Bpd6CJaLxbfFubRo3Ad3IC7uApTRmAqgeUPPUHW2/9hEMBy7Vhuxgguf/lxUlbvRu/xcHTeBI6UV/DVvoPcOvts9hQWYfOqrPr4Qz5+602811zLrCGDOjXXzqIoSrvJ/nqSGTNm0NjYyIYNG4iMjGTWrFm9cl1VVdG5GtBFB6d+dtG+dQwDxNTbgzKe1OtRvMHZ8Uf5s87Wuru2IzbqLTS0k2Leorfg9DlRpYoi+i6RRiBXTgG+FkLsAbai2Sg+BW4HHhdC7AYeQVNFIYRIFUJ8fkr/u4G3/P3H+9sCvAwMFELsA/4H3Hym2imYmCMMOHqpqJA5RdMl1x+t69Y4Drub9P117M20MDw9mg8qaoimlnPbqIjVor/nGAZnEp//YTPr15bQ4Fb9QgLCo2ppKJrKuElfMH9eLtlTb0GnM56motDpDVgvSEDnjKBszRdtXkcIQeQ8zeW17LFtqJ7OqfiMgzXB4zzYcQGfZiI/fheAxr88ycHzriIvaxQj/v4Xtm7S1G1egwGzXs/YtFTuXTifSLOJs4cNYeGoYdz4/e8D8OUbr7Fl3dpOzbWzSCmD5lrcFc4991xGjx7NqlWr2LNnT69cs7CsBhMeEpKCE+RZX7ALgAFjZgRlPKnoIEjCO8aoLQTru5gZOUwfhk9tX/UEfZ8YsMMdhZRyD9CiWoqUcj3Q4oklpSwBFp/yehfQwo9NSukGbuzcdLuOxWrAafMiVYlQgvPFtTntmI0mdMrpGT0jsiJwrAdbUSNMDdwt9Uy2fpXPQC/Ez86g1FbFRmcSF4flYdR3vEtx1VdQr9tL7Za7qKhyMjjDysi56TRVOhi6OJvS4h189Gg9m5etZuHNl7c5TvzQ2ZSyEY+jfXff8BkpNG0oxVfrpP7jPHTxJuyb1hI2cQgR505t92FpHqIJCvfRPFjQdiS1rb6Jb559A1lZQaatlsPX/IhLLzuPqZedd6LNP3//Bub0NFyDo2loaCQysqWqYld5E+a8g4ikND5f+SUep4OZ53U/+Ko1+lpQKIrCpZdeSlNTEx9++CEGg4ERI0b06DV3HC4AYEhWcNKyqPYanNJAXGyQYkKErt38Sp0h2hQGOKjroD52W+iEGcVXy3MfP8GxpmOkh6dxw/xbifRnhW4WFHavnTBD3xWK+k5EZgNYrEakKnHZvZit3c9Bc6y8lGs+uYaZ4XN57LqHTjsXNSgaB+Au77qqS/X4iN5WxYEEA+eNSOSvez/FSwZ3DGy9EPup1Hx0BZ7Kw9Qf/j4VjcPISDCz4P4ppz2w0gdMIW7gy+RuSaV8bh5Jma0XNdLkuebG1x6KXkfSzydR8oeN2LZq/v++Bj21r9yCYeAEwqfOoOGz99CnDSRizlwiF52DebiW1NA0RPvtOd56DYniw/ns/OX/I6I4nwz7yUR/gxafXoujuqaRL5wRjCs6yuahUzmw/xDTZ2jOdA6Pj8c35/PfjYVkF+xkvq2OcWdfw5ZNm1i5fgMOh51zL76s3XvsKn0pKAD0ej3XXnstb775JkuWLOG6665rEYMRTPKKNNvYpBEDgjKeKgzo8Wm7gCAY5aWiIIK0o4g2WgEH9V1UZamOKACern0Jq89Ck8dB/jt5WMMj8EmVQlX7XzrcDui5Ss4d8p3IHgtgidCEg6Opa+onVVW58KUruPg/V1PbVM/dH/2MRkMtR+0ts33qwww4AbW26waz7RuLiXWqcFYKQghW1akMUEoZn9D2alB11VP7xR3E7vySxGPHaLRpD+AL/9D6iv68m+YjhMqqNze2aexsyNcM1Prwjlczil4halHWiVQTusg0TGOvxZO3g7r/PY30efHk7aH6uUfIv3QRB4ePxJVbjOJPdaK6W3c22PGHvzHwyE5MQlKYMRzXP56l5vd/Y8y00+NDXn91GW6dge8N1+wcOXWagbHB5WH0X77kxU8P43R4scZoMSiRyQO46977sEgf67fv4pO33+zwHjtLbxuR28JsNnPjjTeSmJjI0qVLOx0E2BkqinKx6SJIium+4RlAr4BA4nQGycao6CBI70usv+BQo6drgsJsPo9o68OsvugrNt66hSGuLJbp1/C+8zO+cKzmsOsoye54wmr6dk3/3REU/oeRo7Frb+je3MMU6o+Qbz7IOe+fTa5pHxZvBFWy9Xz8HpMOxd61a0kpkRtKKLTqmD01Hbe7liPeZMYb2/fHr/v0BmI2vKP9feGvGZ+lldcs/OyzVtvHJWUxfHYDtUUp7N+8rtU29Xs134P46YHVvog4O530v8wi9ZGZKKYSjAPnEnHBbcR8/16GbljF0O2byHjhTRRrIiApvPEW6j78EoCSIzso8K9Gm6mtqCFt32Zyxp3NjO0bWbTyA8YvnsPMGy4+fZ6lFbxV5GOcp4pFF80D4Ji/VGejy4vP7iUyJZyD95/Hzy7WdN1b164nOi6eH//il4Qrku2Hj7Lk5RcCus9A6WvV06mYzWauvPJKvF4vTz31FF988QWeLj7g2mLT/lzCfY1kDA6eeiu57Gv26UdhDus4yDQghBI01VOkwW+P9HbOLuf2+Ph8xzF26CXDLYOIi9UWNy9f8wbXWi7ljWmvsPHWzaw5/yteyX0YS2NIUPQK5m7uKNbnaA/dIb4xpLoHcHXkzcy2LKReX4Pb18qYEUbMPrVLK8rDe8pJrfNSNSUek05HUe1+3MJEfCtCSUpJzRc/wPaPNGL3ag/7uonnEzPlftIW3QJAbVnbwUBnX3wJRmstmz4o4MtVP2Tr1mfxnrKNVp0qqt6BKaJzcQeKopD484uQXidSTSD5Nz9EMZtQDHqssyYx4KP3iDj/Bny1xVQ8pKX4iji8G8eC81j+yps4HQ5eu+hGys6ZicnjIurqK9u93l///RFV5kh+c8UkwqMiibQ1UurxIqXkF19ou6K75g7CpFeYMG4EjZYYnJs+Zc3j91K/8wvu/tVvidIL9hcd581nngqap1J/EhQA8fHx3H777YwbN44NGzbw6quv0tAQvHQzn3yxBq8UXHFuYAuLMynJP8jOp2/i2IFN1NdUUnFkK5neQqozg2dDkooOoQZnR6FXFCw4aPQFNp6qqvz6iwOMWL2HW+urMUr45biTdWWio2L43dV/ZOwIzfyrj9FUvt4gBvB2he+MoOjujmJfzV5MXgtLbn6DFbd/zO8v+wWZEelIof7/9s47PKoqb8DvmT6T3hMCJKHX0AJIEQRBaYKoK9gWy7q21bWs5XPVtay7uuq6zXXFBqwFK6isClIVadI7BEggvfcy7Z7vjzskhJlJJiE0ue/z5CHce8+95yST+zu/TkZBltf1hmgrJiGoakMpj8LVWZSYBWPGJAOQHHMRBumk0uBdAbZ8x6tErvuEoMpqnEYd5RPuJnz6Qlx1dpa/uwerroJeV4z1+yyj2UqfsRJ7RUfKD7uorHqZb5de1tD/QLj1SNE2gWcItSFdpSC9y16bEmPo+OoTRN7+KNbJNwCQHaNGySS9+DwZgwYzLH0LAO/0ncotPzk5UOpb4O3+cRsfueK40lTKiJH9EEIweflnJC5+h4G/+5D1m3OJSwrlztRO5BzZR9ZfLuLWGLW21OZNh1j133ewWK3c++jjRJmNHCosZt4/Xm0XYaEoyjklKECtQTVjxgxmzZpFYWEhc+fOJSvL+zPcWnYdyUZffgxDXDcSY8JbPT7/6AEM86cwqOgL4j6aQtg/uhH7gZrDGzOs7X0jTkbqdO2WDAgQRB1VASgUxwqreGrlAeYbHYxw6nkjPJotl6TSIzHM7xidxYDOZsBdpgmKM4LV48Cub6NG4RQODIq5SfhoclQyAOl5R7yf5ym3UHlCuYtAyMksp0tePYf6hxNhVYWbQWcgVldBnrOpE95ZW4Bx1Su4DDrk4zkY/6+E8NFqRc2Nr39CaX0ME6bqsCU0H30SkaDuXlxyOHV1HbFaj1FcrIaZ6mON6J1BVGUcatU6ABzH8hGmOAzR/vs8xz10M8mvPkH6gMFYXU5ifvqJkr//i0PDRnCg30CmXvMyn3e/BICDZb5t1C9/9hMWxcXv75nacKxz3iGM0kX3mkNEdQzmi18Oo66mGtd/ryHGnU922r1ccd/dpES5OW41MBiN3PPIY8QHWTlWXsX81187JR+D2+2mrKyMiIhT63lwuujduze/+tWvMBgMLFiwgPx838EEgfLJ16uQCOZc2frdf2VpIa75V2LGzoY+v8ckGt+8B4096derdU2BmkXo2s1HARAkHFS5m3+V7swq45Idh3hLb2dijWDB5f2YMagjNlPLPdD1ERZcp+DvbA8uGEGhN+owWfRt1ihMioUaUznl1Y1qes8OaqTQ4cIMr+tDUtRdQk1262rApK/IoFYPaeObRoxE6uoodTf2t7AX7UD3cg+CK6qp7DcWYQpuiAipPJLOjoNx9Ek8QuepLVe13LfpGBLJ+OlXk5L8WwCKilTBEDl4MG5jNWXvZFC0dW2r1mLPKEDo9Ji7tRzW2OHWW4gqK+X7f/yX0ZdfyhUL3uHKTz9k9SPjmT1RTYoLMXvbaZctWs1qUyKXWyqJiWs0jyVaVHt2L30ZW34zlvggMzvee5QkmU32pa8zctbD9Bg1BWEwNgmk0en1XDP7Oowl+RwtKjmlrObS0lLcbjdx7ZRPcDqIi4vjtttuQ6/Xs3Zt636/J1JT78BZfBR3aAdSOrS+wMK+9x8m3p1P1uXvctG1j7DVpEaruaWAobe3r1bWjqYnAJujjuJavVqtwAd5JTXcuTsTmxsWdUxk3uT+rSpoaIi04NZMT2cOS4iJuja0Kf3pwHbWu1bS2zWYiJBGNbFLx07oFSOZpd7lIEKSQpFS4mxFNnhlSS3Jh6rZ2T2YpMimBeCCdS5qpEcryt+Ee/4k9AqUjvwFETMWNbl2z6I1gCBtzkQCofBILcJWR2xCFOHh6ku5vOKguo4O3Yi4MwW9y4r9Y0n2isVeJhlHdSXFO9ZTdmg7iqe3c/mSDZR/qfpU9GEtR0yljhqJWxgIXb6F7bnlDcc7h1qxexL4wizq+u0uhas/3sKcxdt5aY36s3/olqZlxiLC1F18WPEh8guKObpvM2m5H7Ax4gr6ekpCSykpqXAQdlIm+YH1P2ApymXIwIFs2rSJb7/9tk3C4ngxwOPlxs9VQkJC6N+/PwcOHGhzEcEvvt+GGRfDBvuvK+aPI7s3klb8BZvjrqHfCFUb6XHPRxT/ege6p8vocVn7ZGQfR+r07Wp6GuTI4KCxM2v3e/sQ07PLmbj5INlG+FtCHCO6x7S6HIs+woyrrB7ZjsKttVwweRQAoVEWyvJb5zMorSrjzh9vx6zYeGnan5qcMxgMRLpjyKn1LiOtM+mx6wSyInCVcdvyIyRL6DYuyetckM5NrcuEq64I5k/BbHdRMfOPRA64t8l17toq9h2OIik6h5DOJ9do9KaitBp3tYmo3urLPyGhHzt3hlFX9zWK8ig6nY7QxF7o7jVR+MEWTN/Fc2Tffwkf0wN7fgn2/ZUY8uLRSSNQRXnIF4RdlUTNysMIU2cUewWWvj4LAzfh8NzP0EsXhTGDWPevnST83zDiQlTfxvHdZEG1ndIgMxPf2UBJVhUgwRLNMylOOnZtal7TBwdDAeiQ7N62E9vhD0lAR4/rX2q4Jm/TMirqDQzr26fJ2Mztm0ns2ZtpM2ZgMJvZuHEjbrebKVOmtKq4XkFBAUIIoqPPbr/yQOjfvz8//fQTP/zwA5deemnLA05i1+5dCPRMHuWVm9siRSv/RTwmes9u/PsKDosiOOw0NV0S7SsormI33yiDeeYoLO0di97zGflkXQbPVZbj0sGSbkmkJretEKUhwgJuiVLlQB/WuvI47cUFpVHEdw2jJLu6VcUBl25dg0NfzwN9HiYpPtHrfIIxkXyZ5XPH6bIY0Af4LEetk/hd5WzrZGFgkvcHyqqDKmllwbfPU+2yUXfli4SdJCQAjiz5hjoljH7jA6thtO2HdASCXmnqi1an0xMRcRVWay47d33VcF1wYheSH7wKRpRjyu1E/Qcu5MowdGXBuPsWYLzGjZhQiXAZqJ1fizB1RpgL6fzqNMydmze9SEVSvegT6oPjiL3/CiLKXPznX9saNIkreqk9kZccKOByj5CwRJoBQUJ9OdfNmex9U32j7ffo4SN0K1rOvuDhRMQ0Zsrv/t+HGIWbXlfe0WRoeX4e0Z2SEEIwadIkRo0axebNm1m8eDFud2BhkA6Hg61bt9KpU6eGLnTnMp07d2bgwIGsXbuWJUuWtDrPwl1VghIch9nUurXW19XQu3Q5e8LHEhbZfh0hm6WdNYpgvYXrXZ+y2wrvrc/ErSj8e+1h7rVXEIbgg+6d2ywkQHVoA6eteVhAczhrTz4LdOgWjpSQdyTwzls1nnLE3WJ8Z5n2COtFmamIwiIff1ihJqyKxBVAjPWWNZkEuSThY7yFEUCk0UQtNh4Pv5l3+9xCcOodXtdIRWHbejvhpiI6jw0sPDFzVyGKzknfoY2N4QcPvhe328ixo+82uVan19NxxhXEPtwH40w3wb8Oo/OT00i58Tri0i4hccJU4u9tLCKs7xdY3Pux77YQXJSOdepMJozsTOjkjkQfreMf76q1icZ2CkcXZGDJqkyKsqoYN7ITt8WpNtubupkxWXzsshQ3SEm1PojCfZuJpRTZr7FUiaO2mv3phfToZMYU06jBKYqb+ppqbGHhgKrNTJgwgfHjx7Nz504+/fTTgMqG7969m+rqasaPHx/Qz+BcYNKkSXTv3p3Nmzfz3nvvBWyGUhQFk2LHGtT6PIc9qz4mlFosQ85YNZ92FxR6fRAjxWoG1cMT9RWMWrqDZ51VDK+FleP7Mzil7ZpR/eFyyhYfQh9mxhhz9lKzLyhBEZcSitAJ8tLLAx6TEqs2b8/IP+bz/IAO/UFINh3a5nXOGGPDKASVWc07tBWXG9umQvZEGbi4n+/aUM8MmMJ9ZrWJ4Pi+U31eU7JrJ0V1ifQfpCACsIMqikJ1Hlii3ZhO2AmaTGGYjJdgte0hJ8e7naYlIp644ZcQ3iXVyxRjjojjiYSj1G38Nxu2/tjiHAAqVqwGoPOv1R7Gc6b3oCI1DOvWMg5mVaDT6ejfXf1ji0gI4q2pfen36ds8u+1dbrttms97SreCAByhcZiKsnll38XkrviaBbdN4ssHZ5KzbglORUfKwKblyuy16sbAbGv0EQkhGDNmTEPZ7oULF7aYqLZ161ZiYmJISvI2I56rWCwWrr/+embOnElpaSmBNgqrqK7DIBSCg1svKJSD31JGCH1G+v5MnxZ0BkQ7mvsN+mAw1PHvIV2YYNcTpQhetoTx2aRUjAFENfnDnllBybw96MPMxNw9oE29XtqLC0pQmCwGYjoFk9uKqq5JcapJZl+eVxV1AIZ1V9tr7Mzb7XXO2kn9w6lsQYPZtTGHqDoF54h49H6iO6wGA0ZPVMXADr61m/3LtqLDRdcAk50O7c5CuI107O0dvtmv/30IobB33+sB3es4u0rS+Ta1H18lh5Hy1SKyjmS2OMaRlY3TGIQtsdFEdc3VPXDr4IslavTVXyf1YfCgeBbfPIytK9eQnL6fyBmXY7Z652iAql0h4ZpfNzpC92U5KKo2kJ7jZMtitdWrclINq9rycgCsod5NaEaMGMEVV1zBoUOHeO+997D7abVaWlpKdnY2AwcOPOdyKALhuKlMrw/sJWd3ujzXt97lmVixjQzbAPQBdGxsN/RGtcpMOyVV6g3q33mixc27U1L539SB3DgiBUMbWrUeR6l1UvzuHvShJmJu74/hLPkmjnNBCQqAiPggqloRavbaarWkQ4Wr3Of5DhFxhDgjOFDh3bQnzBMiW9tMiKyUEvvaXI4G6xg3vHOzc6l2qvOWJu+WmPVFhew7EkOX+FyCOgW2i929PhOAgaO9C8RFR/XB4eiJ270KtzvwSLGNJWriVt/7rgcEW/7xrxbHKIW5uMOa2qd7xIVQ1TsE4+4Kckpq6Rpu4/NZQ0gKs1H8+uuUhEcw7mb/5grpdiN1gkijm5tee4+r7/kVN//h96T1Uv+ojxYp9EoOpde1DzUZV16g9gUPj/Ot2Q0ZMoSrrrqKY8eOsWDBAurqvEtE//TTTwD06xd4r/JzieP+tkAd922VhcW5mXSQBdgT/VcMPh0InSoIpa+KCm3AYFQ/U87a9slwl4qk/KsjSLubsMkp6ENMLQ86zVxwgkJnECgBptsv3bya5Y4vGeAawfO/eNzvdZ1EF466vXsp2BKDUSS4iv3Xm8/YU0SHMid5g6MJMja/g9tm1zO4LhOD6aRwU0Xh4II3ccggBs8Y2Ow9TqTgcA1Y6kno5DsqJzp6EkZjLYcPr/Z7D5fi5o39yyitL6e4roQnc1UT0bA+g8i8eCwdV6+kqqb5EGF9eSFEe7+Yp0zrhsENHy9p1OZ2rPmR5L27KZ59A0E2/zbb5MhYbHYnn/7xCXZ9+SGJwycR1WcEY556n4tH9WDSjLFMfn4B4qRdc3m+R1DE+y8Pn5qayrXXXkt+fj7z5s2jurpxI1BZWcmmTZtITU0lLMx/xu25zHFB8c0337BixQpWrVrFp59+yjfffMMPP/xAZmZmk+uXf6u2n2mtNSf7wGYAIroG1l+l3dCrL17pbp8igwajWvzQZT91QVG9Ppecx9dSu62Q4IsTsfQ5TZFfreTCExR6XUCC4oedm/jdnnsJdUby2qxXMBv9q349QntSaiygrLKpiUnodTj0Air9RyvkrDpKiUkwZmxyi3MSUsHka/f23ZMYCtRyF8bEHi3eB6CyrBp3pYmIzv53K716zkRRBEeO+C+U99KeZfwhL5Znd6/iv4cbfRIhRhuJV15JUH0d67/6xu94t9OFubYEXZx3N7TBKRHkdneyvmAuWWUlAOT8+3XKQsMYd9ucZtcXYbEy5kg+gydPZ/vS//HVX/+s1l3S6xl231/pe/3D6E4yd0gpKcvLwWwLwhrSfP/j3r17c91111FSUtKkXtL27dtxu92MHeu/bMq5jsmkfiYyMjJYu3Yta9asITs7my1btrBixQrmzZvHqlWr1J9XWRnpB9UKyq01s1VlqwUnE7sPbNf5t4hHo1DaqRe1waQKCmd929qhnojTU/InfEZXwqd2abfeOafKBZVHAaDTt6xRlFSVcfe22wB4oN/DhAU3Xy55QIdUFh/6gA0HtjJ56Lgm51xWA0Y/SX6ZR8tIyaln05AIBgQ1b4OUUpJhiGCsPKmC7PrXYP2/OGRWX+aBhP4qisInf1sLGBkwxn/PgNDQTkg5HoNxJQUF+4iL864I+nmJ+kFeWJWCqFTors9h5ehJGHR6Bl06lo1h4dQt+QpmX+3zGfV5xeilCxnrO4T2UOTH7NNt4qmtb/NowgxSdmxh/y9vZWRIy45TvZSMu/nXhMUlsGreGxxY9z29Rvl+gTvqalnwyL1UFBYQk5QS0EuvW7du3HTTTbz//vu88847zJgxg7Vr19K9e3eios6NnWBb6NatG3feeSdRUVENrVyNRiNutxu73c6yZcsahEdOjppDZJd6QqNal1goig9QTgjhkW1v7tUWhEejaD9BoX4WXY5T1yiE1YAw6gge0T5tZNuLFjUKIYRFCLFJCLFDCLFHCPGM5/hAIcQGIcR2IcRmIcQwP+PDhRCfCiH2CyH2CSFGnHT+d0IIKYQ4I1lJer1AcTfvxLrjEzU/YYbleq65yHdUzYmM6Kmqztuydnid04WbsSKx13kLi/1Lj1Cnh5ETfDcNOpHNBzZQaIxgmO0EU8nuz2Hp49B7OnmVsXRLiyU2qfmdMMD/5m+gvsBE/AAdfdOaf3Zq6v1ICZt+ugOXq9Gmu7eqlvhV28mS8fTQ5TDYcJQZwcf430VjMHrMOXqDgYJLJ9Jl62YK8nzXEarNVrNZjTHev/6dR/azX6j2/s15X7J1/ru4dHpG33JTi2tsaIoBDLp8KlEdO7Ppy8/8Zljv/WE1FYXqXAzGwG3CSUlJzJkzB7vdzvz58xsS885ndDod8fHxGI1G9Hp9E+e2zWZjxowZpKWlcfjwYWJiYrjyhtv40D6YyJj4gJ9RXJBDbOUeiixJbXdytBGh8wgKdzsJCrPH9OQ4dY1CGHRIZ9uKcJ5OAjE92YHxUsoBqD2vJwkhLgL+AjwjpRwIPOX5vy/+DnwrpewFDAAa4i2FEJ2AiYDv2NPTQCCmp0SrGunkUgJLlkuMjifEGcG+cu9QUmOsDYMQVGQ03W3sOlBEnyO1pPcJJz6i5RIXv82sJNZewuR+o9UDlbmw6E7oPAKuepOQCAtuZ8tRHFvW7OfoxjoMUbXM/PWYFq+Pj+uDQT8Wmy2HtWufbjgeoW8UfE93i+fri2fwn2FXEmpuqn31u/F6DIqbDe8s8Hn/+jyPoIjzTrZ6ec2r6BUjV/V4GNwV1B9YRsZFo4hJaPmFJJ1O8LzghE7H4CkzKMo8QvbeXU2vk5KtX3/Bynf+Q0SHjnQbehGDA6iPdSKJiYncdNNNhIeHc9VVV52zRQDbCyEE06ZN49FHH+XWW2/FFKT6Yiwt+NiO8/1HrxL9eh96yAxKE9uYZ1J0ED6/AyrzWj9W7wkzbSeNwmhWN2cuZ+vquvlCGDyv5AD9qGeKFgWFVDn+EzB6vqTn6/j2NQzIPXmsECIUGAO87bmXQ0pZfsIlrwKP0Ho/WJs5bnpqTmL//boXiHV08tm9zh/J+m5kur1DaIM8O/yqzEZBoSgKxV8dptIkGD2jZZ/CvsObOWKOY5YrnegIz0vyyGpw22HqX8FoISIhiLL85p1ze7ccYf3CLDDbufbBiwMOfxw37m3q65Oot39FfZ3aijTBFkY45Uy1HWV8on9nZPc+vUhPG07HTxb61CpqjqnHgjo3vvxLK8v506K/sU23jsts05nRU32ZVNrqSbjh+oDmrNTUoDuhK1/viy/BGhLKlq+/bDgmpWT5W6+xav6bdE0bxo1/fpUZv3uCXiNbFqAn06FDB+6//3769u3b6rHnK1arFSEEdZ4MemsLOQPVtbXkPd2VMfueBqAeE2mznwzsYS47bJyratHLnoDXhsLOhfDuZCjxDiRpFoMaUq04W98CwPft2l9QSFf7hO62FwE5s4UQeiHEdqAQ+E5KuRG4H3hJCJEFvAz8n4+hXYAi4F0hxDYhxFtCiCDPPacDOVJKb3tN02f/2mPa2hxoAlBz6PSqmqs0U2BLSold1GLW+Y7R90XPsN6Um4ooLC1pcjysWzgA9TmNH6Lv1x2je7GTolHxhAa3HB+9PE+Vwb8aekLtpmqPryJCDYWNiLdRUVSH288H7PDebFa+nQ56N1c9mEZEVOsicmLDZ2Mw1LFx6WUoLjVM1ypc1ATQK7j3449hcjhY98cXvM6V7VT/yKNSVRPY0YIcpn98JR9Wvk2KszePTb2f1PAEjC5JbqRgyLiLA5qvUlOD/oSkOaPJzICJkzm8ZSMlOVlsX/o/Pv/zH9i5/FsGXj6N6Q8+jslyFpsSn8fUHxcULWgUB9cvIYFiAPYZ+6J7PBd9oGa+RXfANw/Dp7fAun9CbB8Y/yTUV8DccXBoRcDzFQb19yxd7RT1ZFO1aLe7HQSFUX0/yQCsA2eSgASFlNLtMTF1BIYJIfoBdwEPSCk7AQ/g0RpOwgAMBl6XUg4CaoDHhBA24PeoJquWnj1XSpkmpUyLiTn1WjANgqIZ1W7V7h+pMJYwKGJwwPftGa3mIuzLbtq3wRRlwQEonhDZmhoH4cuzORZm4OJLW/ZNuN1uFlYb6eIoICb6BMdzbYm6MzKqu+aI+CCkIqko9FanHXYn37yumlym/qY/HZJaX83UdbScw4eG4got5eCKKUhFwSgUHAHYUrv36cX+6VfRa8VSVnzXNFu7IrsEt86I0bP7f/Lr56jRVfJy33/w5a8+JiIkjAPbVuA0CGIqZMCx/apG0TTfZMBlU9Hp9Mx78C5WvPM6xccy6TNmPGOuvxnRimJ/Gk2pc/gWFFJK9ny/CJ4OY/Pcu6nL3NxwLvTKl5tUA2iWskzYswhGPwCz3odZ78Fd62DM7+D2lRAcA1/eC/bAfASinTUKvckKig53O/g8jldUOC81iuN4zEargUnAHOBzz6lPAF/O7Gwg26OBAHyKKji6AinADiFEJqoA2iqECNwb1kb0HtWuOUFxIEfd5TpF4IlmNs8Lu97ZNJlPCEG9SY+xSnUEr/1sH5H1EsuMLhgMLf/4j+Qf5rA5gXvCnOhONBXVloItqsERGJmgvhR9VcfNzypFOM2kjAwmpZfvWlLNIRWFrYcK0JV0IEnpS47xKEdWXc3g9WuIWb09oHvsuOxq6kxmDr0+r8nxiI4R6BQXboeTrMI8tov1jDVP4vK0xuixTzbMRe+WXLwvcKenL0ERHBFJ92FqLEXfSyZwx38WMPmeBzFaAtccNbxpND01/Tx///6f6bvyZgDSct9nVNYbAOwa/RqJfVvRKnWPp4z+wBug9zTofUWjAzwyBa58XfXZrX8toNs1ahTt46PQ6XXoFDNudzsInuPhsGexpLgvWgyPFULEAE4pZbkQwgpMAF5E9UmMRRUc4wEvA72UMl8IkSWE6CmlPABcCuyVUu4CYk94RiaQJqUsPvUlNU+jRuFfYq/I+Q6DMDKow8CA71teo/ogwoK8Q2llpAVbXjXbd+bRe28lu3qHMLVPYM1s8soLgBC6hp1UfdJZ22BrBQiPUwWVL0FRUaKqxGFRLTvNfZGzfQVF7hCm9Y2m6/iXcCwbS6ZpJ923qYL0D/ffgzWlGyUF+XQfNoJfX3ml1z22uXVEDBnBuE3ryM8rIT5BDR8N7p6EWC+pSj/GB+lfI4XCjUNnN4yrr61imf4AQ9MhrFrBkZODKbFlYeeuqcEY773vGHPDLYTHJzBsRvM9uDUC57igONGZ7Xa5GHTIOyv/sL4LianjvI775cA3sPxpiOkFUd18X9NpGHQaDodXwiWPtXxPj6BoL2c2oAoK2kOj8JiezjFBEYhGkQCsEkLsBH5C9VEsAW4HXhFC7AD+BPwaQAjRQQjx9Qnj7wXe94wf6Ln2rHE8Pr45jeIYhxliGM2koYEnTWWXq36EFB9tR62dQ9EJQfQHhyi16BhzTR+va/zh9PhJDCeXGzBaVQcf4Kh3IRVJcISZch+mp+oK1RYbEu5d+iMQtq1biQEn/SbMRuh09J64in6hN9IpRq2YG5yfhXHdCuIP76Hqw7dYt3ev1z0ynU6+HjwWq9vBurkfNBy3dlF9LJX7M1hetJR4RxJpPVIbzn/97T+pssI0+gNQtXRpQHP2pVEAhMbEMnr2LzFZ2yY0Nbyp9yEonLUVhFLDDwk3w9MVrO3xKNvSXqDrk9uIjG2FVrtlPgTHwS3fNB9G22EQ5G6H4pZb9gqP9i+d7SkoLLiVduhCd45qFIFEPe2UUg6SUqZKKftJKZ/1HF8rpRwipRwgpRwupdziOZ4rpZxywvjtHh9DqpTySillmY9nJJ8JbQLA6bGnGs2+HW/2Oid1hmpqlNbFRGdVH8XsthIb6u1HCe7TGC7pmJ5CSFDgcfphnh1GycmCzWBp2BEt+dcO3nzgewCqSrw/rFWeXtNhka0XFC57PbuLoU+4E0uomusg9Abi0p4hNrY7Rp2b6/49j3HP/53gGdcBsP6ZR1i5aVPDPaSUlBmgtktPCmI6YV32FW7PH0JIT9Xvsn3nevJNR5kQ07Tf8uKj/yOmWsek36gNh0reaVr63B9KVRW6AJLyNE6dUE/nwaKqxgoE5cXqxqnepGqOo69/nEHT7mrdjasLIX0ZDJgNthb6OYy4G0xB8G3LGoUweARFe2oU0oIiT/1+De1Uz5GM7ONccB48Z70bRKOgUNwK+9fnkbW/FJfTzY8b1SCsIfGtqz+T48wmRknwmdF7rNLOUZtgQbKRYYNbl3G53/MH1yXqpHEGC7js1Nc4yTuklg6pLrOT66OEeo2ny15EbMvJeCdz+Kdl2DHTv793gbug8Aicip4opY7B3bpyx/U3MOix5wDY9sqzDU1+9pbWoBh19Am2YLjiSpJLsvjxa7U/c2TfJJymYBYpy9ArRm4Y9YuG+2cc2MT2iAqmmoZgTuyIdfBg3MXFVK9Z0+ycpZS4q6rQt5BRr9E+DOgUDsC2Y+UNx+KT1TBhY7V398eA2b8EpBsGXNfyteGdYcgcOLwCaprfc54uQdEepidXQQ3oBIbws1st9mQuOEFhr3NhshgaXujrFh1mxfx9fPm37bxx7xo++F71z09MbV2tnmJdHrE6b5W6NLeGLe+nc+swG5uHta6sg1QUPi130dFRRLcOJ1V41RvB7cRe6+1wPzlHpK7aiUQhJKz1GkX6nm2YsNNluHe2cUyXngAU7VrXcGz8oEEUj1a1gmef+wNSUfjmmBoyPCYujLTbr8ehN5Lz/kJ1GSYjB4f2ZnO3ciYaJtHxhOzej1f9EyRcO+F+AOKf/gMABS+82OycZX09uFzoQjVBcSZIjrKRGG5l6Z7GPJlNH/8ZgOEli9t+4/TvIDxJ9U8EQo9JIBX44ZVmLzvuzMbdfh3jdFhROHXTkz2jElPHYESAyYtnigtOUDjrXTjqXCx7ew/fvLGLHcuz6D0ygUl39GP49C5sTFLbf0aFBJ5d61JcVOvLiTU1dVA76lx888YujGY9o+LCyHcE3oJ1Z8ZOBn23mg3WLtwVVO0dvqk3gdtBWLSVxB7hRCUGc/MLo5h27wAvrcZe4wK9q1X9no+TWVRNkqUWfbB3iY3Y/mpOQ+H+LU2OP3v3PVSERRG6bztlhQWsK64CKZmcFI0lIpzCIaPpsetHsnJVAbKmbx5B9fDo5b9puIfLYecb9w4GV0TQKUX1WVh69MDUtSuOjAzqdnn3/ziOu0o1G+rb0EhHo/UIIZg5KJEf0osoqKxn74ZvGbZfLdSwM/nmtt3UZVeTSrtfFniJj84XwdBfwYZ/q2P9oPP4KI77+NoDPRYUcWqCQnG4cWRXYU4596oOX3CCoudFCST3jyL3YBlHtqkJfGOu60HXQbGkTUnmXxf9B4D7Vt2HM8A+DAVVhUghibWqgVzVZfUs/OMm3nzgeyqK6ph0e1+6hlo5Vm/HFaCT6okDR3EIPS/bcrl5xBXeF+g9CfKKm469IynJqQYBSX29tRZnvYIwtj4uu6rgGMWuIJITfedd2Dr3JdjopCgzo8lxo15H7JgJuIJCKayuZq/Tga1OIdaq+mZ63HYTNpeddW8uRFEUNgcVMLI2kejYRvPa8u/mUhIsuarrzCb3jrjhBgAqvv6f33lLTwtPoSXQnTGuHtIRRcL7G45SsX5+w/H4i29p2w3Tv1Mj+7pPbN24ic+p0VGL74a6ct/XGNpfUOiEFUWcmunJnlEBbonZk6R7LnHBCYqOPSOYes8A5rwwimseS2PWE8MwnKDmje05ir+M+QvpZenM3zu/mTs1cqxItcPGh6gahZRQ4mlWNOrqbnToHkGK1YxLQo69+WYpDpeTB1d8wSZzJx4y5XHj8Cm+O4cdr1ejOOkyQHWgH9xU4POernqJvg0mz6NbvgMgud9w3xcIQUyEicIi76qZJfsPUNe5BzvySyk3C3qfkIHbecxFFEcnYvtuCQd2fk+1RTIovmly46IDnxFaJ5g08c4mx8OuUIs01m36ye+8Zb26s9OZz37DlwuFlOggJvWN590fM4moPsQBfTcOzFhCUtcAzUYn4qhRQ2Iju0KXVoTSAphscNVcqMqHrx/2eUmDRtGOpie9zoqiO7X72Q+WgUGHObn1vsTTzQUnKI4jhCAuOZTojt7mickpkxnfaTxzd86lpK7Ex+imZJephck6hCbgtLtZ8HijzT51vBoum+TZTWfWNS8onl+zmA90SYy1Z3CDD79AA55SybgdRHYIIqFrGLtWZVNf460FKU6BweJffXe73Xz00Ud88skn5OY2luzKPHwAEw7i+/v31+j1eqrrm2pJR9MPIj29Hj49Wgg6wZyujVqJEALjjJl0K85k6af/BmDIoMaeyblH97IxvJhJsi8mS9MwVn1ICLrgYOxHjvidk+JpUSrM55ZD8OfO/RO7Y7fX0ct9kKLo4fQcdLGaGLrmL3B0Xcs3kBIW3QWvXQSlh2HKX8DQBmGfOATGPgq7Pobdn3mdFkaPr65dBYXtlAVFfXo55pTQc84/ARewoGiJ+4fcj91t529b/9bitfkVqqDoGNWBHz462HD8zn9d0uAvSLGqL62MOv8fpo83LuENXXeut+9j4eVXYrE2s7NoEBSqYLhoZldqKux88bdt3kl3Lj1mm/8PX3p6Ovv27WPPnj28/fbblJWVgZRkljlJCnahN/gvtVBQUk9SXNM/5g1rVjV8H1WQgdVRz/Skpj6OwXf8khqTlWXhe4mo1dGrZ2P1+Y+/exW3XjD7knt9PtPcsyeyrg5nvm8NSnq0NmHSBMWZpFd8KM/Gq9FscaEes99Xv4VVz6vF++ZPb2oO+vJeeLU/uD2+u7wdsOMDNdJp9gfQbQJt5uKHIDENljwAFdlNTh0vM+52tU8JDwCd3orUO1DcgfshT8RVYcdVWIul+7lZeVgTFH5ICUvhtn63sfjQYjbkbWj22vzqQnSKHspN7Fufh84guOOfYxvKhQDEm41YdIJMP4LiWM5+7q9JoG99Nk+Pntxy4xydxxzlScTr0C2cyXf0p7rUzqJXtlJbqR53OJwIxYA5yP/LvqJCDa/91a9+hRCClStXUnV0O8VKGMmdmk+OMhkFbqXpXLNz8zC4nUSNGkt8ZSlX7PiRl7cdbTouNIQjs64lK0ZQZlPQCfVn5Xa7+Mr+E6llIXTv7bvMQ/AllwBQvmiRz/PSof6MdRZNUJxppk1UfQrd09+C7C2w70vo7wl5zlgDLybBX7rAc7GwdQFUHAOdZxNT7vmMXPch9Jx8ahPRG1QTlKLARzep2ooHnefb8qodVFbuPLXneDDoVc3XVd824WNPV9PLLD00QXHecceAOwgxhbD40OJmryusLyDIEca2pVkg4cZnRzTxewDohKCzxexXUCw+uBNF6Lk/IYjQ4PA2zTc5NZorHxqEo97Nhi/UelXHy3dYgv1XazkeDaXT6Rg6dCi7du1i7xa1eF9yX5/9qBoIDzFSXN7UnFalgEtv5N6J4+hw6SQia6vYsHtjQ17FcaY+dE/D93N/UpP516yaR2Gwm5kd/ZvdwmaqDu7q733nUygeH4VmejrzBPe9HH6zGayR8Jan18SQW+C6hRDsCX2uLWlq9pGeQIuM79UNUIT/routIqorXP485G6FQ8thw+vw9SMITw8LxRLCtu1zqKzyjqCrq8vh8OFXyMx8Haezwuv8yeg9DnKXvW0VZOvTy9GFGDHEnZsVAzRB0QxmvZnRiaPZnL+52etKHMUEOcIoya5mws29CYn0XWQuxWYiw4+PYlWdgS6OQi7vF1gZ7YaIDUPTZ0V1CKbH0DjSNxfitLupLFN3OLYQ3y/NnJwcVqxYQVRUFLGxsaSmqqGo3+xSk5biew1tdhodu3WlvF5P1TG1aZOUkpQjRxi6aRO5mzZx++jhHIpJJO3oAZ546+MmY4MsISyb+QMGV0f+uftJVh7Zzid7FhJSB9Om/NbvM43RUQibDftB7/4fcILpSRMUZ4fo7mrJjR6TIDQREgerGkKaJwIqPrXp9YvvUov6bV2gFv6ztKMzt9/VENoR3r9Gzdre9Aa8qQqwzml/xWAIYdu2X1JV1dh0LD//CzZumkLm0dc5fORltmydhdPpVVCiAUVxUOBW86/KluzHmV8TcIc66VKQbgV7ehmW7hGt7jt+ptAERQsYdUbq3fW4Fbffa2qMFQQ5wkmbkkzPi/z3/022mDlWZ0fx8SHK19noLyswGQJsY+7pCYHB+2XYa0QCLrubI9uLGjSKEB9d9KSUfP755+j1em688UYMBgMJCQnMmjWLPhFORht2oW9hPvGpowAo2aH6JSqPZjBs0090OZJB7qLFCCF469e3UBEchznvAFsPNDVBJYSG886k/4Bi4YE1t7M2vICJrh5YbM0ny5l7dEfW1FC3axf1B5o2mDpuehImLerprBHbC67/CB7Yo9YlAxh+B8x8A674W9Nrd34Erw1XNYuLH2rfeZiD4Y41au+KsY/B6AcbSt9YOo5h8KD30OttbNt+E4WFS9m1+1727H2Q4OCejByxmkEDF1Bbe4QjGf/w+4jDR16hTskk3jEL5aCJgr9tJe/PmyhbfAjF7v+9UbUmm9xnN5DzxI8ota5z1uwEmqBokTEdx1Bhr+CO7+6guM67NMB3R78j35nLhPHDGD69+f4SyTYzdYokz+4dmVSkDyXW0IpCYMeLBPqIe03oGkZotIUDG/Ia6jyF+hAUGRkZlJSUMG7cuCbtO3v37s21SWVMCGq5w581WhWMteVqgcDSDz5sOOfcuJH1t9xC0Y8/MnLQEBQJHy380Gu3NSgxiYcGPouiU4XfL0bd0eJzQydeBkDmL64lY8aVlH/xReNJz/3P1d3ZBcWJvwNrhFq3KXEI/GYLPFUGDx1UQ2Dtlao24WnE1a4ERau9K8b9H1z6FIz6LVz6BzCYsVo7M3jQ++j1Nnbtvpvi4uV06fIggwd9gNXakcjIUSQk/IKcnA+pqvIudllcvIpjx94iMfFG+k76Ex0eGU74Vd0wJ4VSszGP4nl7UGqduMrqkc5GoVGfXkbFNxmYU0IJGhpP8JhErP29k1rPFQLcvl64XJZ0GU9e9CTPbXiOBXsW8GDagw3nvs/+nke+f4TU6FRu7Xdri/dKDVZ3Vjuqakm0NO52i8oLqDbYiGlNR1hXPQi96rQ7CaET9Bgez+avM3F7cgnCo73V+R9++IHg4GAGDBjgfX9HTeNOEFCkgkBQkruFpTveZMLQ+4iL6UvRHtXRb4tUP+Smzp0bChmE5+ZCbi6lW7dy2ZdfcvBIH3Q5ezmcW0S3k5L45gwez4G3FQrCofvFfspJn0DEdbMpX7wIpaYWV24upfPnEz7D0+va4xyVyrnV/EXjBKI9v+OQOLhpkerIDm19r5RWIwRMfLbJIZstieHD/kdZ2XqCg/titTadR5eU+ykuXsHmLVfTseMcOibehNWaiNtdy959jxAc3Jvu3R4HQB9qInhYAsHDEqjdXkjpxwfJfW4DSNDZDETN6Ys5KZTq9Xnogo1E3dSnsU/2Ocy5P8OzjBCCa3teS2p0KjuLGyMkNuRt4IFVD9Ajogf/nvDvhsZFzdEvxIpRCLZWNm3BuHSPGmN+UaeW+2c34HY0hsj6oOfweJCQv1vVPCJjmgqK7OxsMjIyGDlyJEajj4goZ12DoDiQ/j/GzUvl8nmpTF82hxeK1vGrJddRV12IyarGpBuD1YzwuGuvBcARHITu0Uew/vUVTHYHWf/7msqCLAAMPnp1b8sq52vzkzz0pZ7S+QtaXL7OZqPrV1/RfeUK9NHR2Pftx1WsanzC4Lm/27/ar3EOIQREJDcmkZ4FDIYQYmIu8xISAGZzDMOGfkls7BSOHXuLdevHsH7D5Wzb9kuczlJ69ngavQ/N3jYwlpg7UwkenUj4jK7orAaK39lN3f5S6veXYBsSd14ICdAERcD0je7L3pK9uBU3Wwq2cN/K+0gKS+KNCW8QYgqs+JxZpyPZavLKpfihrIp4exFDa1qupd+AlI1hhT4Ij7UR3yUMxQVSuDFbmgqVjRs3YjabGTLET5Xc9KVqXDvw302vUCkgQoGOOiuPdphApk7y5rJ7iO6jZm2XHFKFaE2WKgzE5RPpecstSE8ZDYPZRHSnrgC4fLzAF246hjMknJDp06lYvBhnXl7AP4qo224FKSme+6Z6wBPFJTVBodFOmM2x9O3zCiMuWk73br/HYkmgqnovsTGTCQ9P8z+ucyjhU7sQPKID0benogsyUjJvDygQNKj1LYnPFpqgCJB+0f2oc9Xx5eEvuWfFPcQHxTN34lzCLeGtuk8ni4ljJ0Q+HSvK5ktbPwZV7UcsvgPq/EdXNEEqQPM2+G5Djn8Qm15XX1/P3r17GTBgAGY/kUHVQrDLZGLyO/35wlVEqj6Ej27bxcdzNnPjxFcZjZUVFQcJSRmATiiU5alJTfZS1VdhDFU1mJKNqmkqfuRILh46EICv12xs8qyqeidf7cjjitQOJNyj9izIf/75gE1H4VddpT77oOpTaYh68qUpaWicAjZbMp0738qggfO4ZOwe+vf37uLnD0O4mdg7B2DsEIQpORRjfNsaiZ0NNEERIP2i1H4MT617ighzBG9OfJNoa+udT52tZo7VNwqKL7YtRwodd2UtBCS82s9n2QEvzCHgrAFHrd9LjBZV4xAGhZqaGhTPizc7Oxu3202vXv7r8DzUMZnrE+PJ1kOqNDK7e9PWod2sceQIN0dXfoQidQSHq85wW0e1ZImzSC24aN+1C4fFQlivXgzvnUS1KZKCA9t5f9nGhvl8tSOPOqeb2cM6YUxMJOb++6levoKCP78QUJihPiwMhMBVqD7zuDbiqxWqhkZ70ZZgCX2oidh7BxFze//TMKPTRyA9sy3A94DZc/2nUso/CCEGAv8BLIALuFtKucnH+HDgLaAfIIFbpZTrhRAvAVcADuAwcIuUsrwd1nRaSA5LJsQUQrAxmLcvf5u4oMB6Xp9MZ4uJCpebCqeLENx8XWeiozuPYbP+CUIHc8fCp7dCUAykjPF/ow6DVa3i2Dq/pQ6O9wd3KQ5eeuklDAYD8fHx2O12hBB06OC/idKR4AioL+ZiEcRrv1zv9UcRYo3CXpfJvK9eI1IXTOpsNe8hKD4Bp17X8LI25+ZRExfXMP6OX87irbffJX3dN/y9qIgHbpjGwp+O0TMuhIGeBjiRN8/BVVBA6bx56MPCiPnNPbSEMJlwezLMnXm56MPCfLZC1dA42wghQH9+ReQFolHYgfFSygGoPa8nCSEuAv4CPCOlHAg85fm/L/4OfCul7AUMAI5ntnwH9JNSpgIHgf9r6yLOBDqh482Jb/L+lPfpENy6LnUn0tnjK8iqd/Dp+kVsC+3D7zLnqT1/OwyEGz8HnREWzIB3p8KuT33fqMslavbrFv8Vbs1Wzz5ASCZOnEhaWho6nY7q6mouvfRSLBbfiYEAeoOZqV2m8u9fbvC5czJ7atroy4LRhdZijFbDGqXDgcGtoHjuLQ0GhKKQl5fHwYMH6dohhuce/x111lhKDm5l+Y6j7MyuYPawTg3PEUIQ+8jDhM2cSfG//kXlty33ydaFhKB4+lC4CoswxLVNkGtoaHjTokYhVd3/eF660fMlPV/HQ2nCgNyTxwohQoExwM2eezlQNQiklMtOuHQDcM3J4881+kb3PeV7dD6hiuxL9TEMqNvPtV26Ncabd7sUHs1Qyyxvew+OroWyTDUR6cQXttECA6+Hjf+B6iII9u7VbfHUd+rWJ4lRo1K9zvtDSkmlo5Jgo//GP31jB2Is347dqFBqDKe6tITty74m+4fVDACsnToBYKitwY7gjTfeAGD48OFceuml/GLGFJYsnMcrH6/AZEhg5qCm0SZCpyPhuWepP7CfguefJ2jUSPQh/oMGTEmdqduyFUdODu7ycvTh4QGvV0NDo3kC8lEIIfRCiO1AIfCdlHIjcD/wkhAiC3gZ3xpBF6AIeFcIsU0I8ZYQwpc94FbgmzbM/7yjm82CXsCq/DyyjFFMKfkBXVinpheZQ2DqK/BYFvS/FlY+B/OvgGMbIWdLY4GzQTeB4lLLKfsgNiWUETO7MuqalvMSTiSrKosqRxUpYf5r7qSN/B1bfrkTS1gYhmqFD596mE1ffILDoMdpNGDevouq1auxlJZRb7EwduxYBg8ezMaNG/nTn/7E5jWqlmAWLib3iyfc5h3qKwwGEp55FldJCQUvvNDsnEOnqn0qSv/7HjqrtaHek4aGxqkTkKCQUro9JqaOwDAhRD/gLuABKWUn4AHgbR9DDcBg4HUp5SCgBnjsxAuEEL9H9XG87+vZQohfCyE2CyE2F3kcpOczNr2OnjYLO2pV002wq8Z/Fy+DSS15MO1VyN8J71ym1ql553LI362WSYju6bfto16vY/DlSYTHtq7Q2CcHPwFg1bFVzV5XV1ONObcWcx1Ul5Zw3bMv8cu/zyX5xRdx7d9P9p13IWw2LlnyFePGjWPatGlMmzaNoUOHUuz5XQoks4d29vsMa/9+RN1+OxWffU7BSy+hOHzXygq/Wo18ql6xAn1YGO7y8latWUNDwz+tinryOJtXA5OAOcDnnlOfAL7KjGYD2R4NBOBTVMEBgBBiDjANuEH6CW+RUs6VUqZJKdNiYrzNK+cjqSE2dtepMf4m6VKrXPpDp4O0W9WSB1f8Haa8DCWHVaf38mcgrg/k72rX+U3rou7ON+Zv5P5V9/Pe3vcoq/cO29Xp9eikag4bduW1JHTvCUDolCnEP/0HAMKmX4HBZvMsRUdaWhpTp07lmmtUS2MnXTkXdYlsdj4x991L+KxZlL79DhlXXUXd9u3eczGbMcTG4szORthsKNVtq+KpoaHhTYuCQggR44lcQghhBSYA+1F9Esdbn40HvEp5SinzgSwhRE/PoUuBvZ57TQIeBaZLKf3HeP4MGRDauMPvbgow+iE4BobcDMNuh9/8BKmzYO1fYc8iqMqDeu92pG2lZ2RPNl6/kZv63MS+kn28+NOL3PTNTV61rt5NX8DhDjW4O4dx0VXXNjkXMXs2PXfuIP73v/f5jF69ejFk9KX8YuYVLYYZCr2ehGeeptPcN1Bqasm87nryn/sjrrKmwivo4otBSmo2rEdnOzfLNWtonI+IluLUhRCpwHxAjypYPpZSPiuEGI0a0WQA6lHDY7cIIToAb0kpp3jGD0QNjzUBR1DDYMuEEIdQQ26P9xrdIKVs2iD5JNLS0uTmzc2X/D4f2FpZw5Qtqlw9FHmI4AFt9OMfWQ0rnlWLrE16sSEjub3ZlLeJ36z8DUHGIOJt8URaI4mxxvBZ+mdM7zqdp0c8jfEMlV9wV1dT9NdXKVu4EF1ICDH33UvEddchdDrq9uwl8+qrAQiZNImOf3v1jMxJQ+NcRwixRUrpP4W8pfGB1k0/F/i5CIp6t0Ly9ztJcZWyfsK4ptFM5yh7ivc0tIU9XH6YoroipnedzrMjn0XfTCmR00X9gYMUvvgCNevWEzR6NIl/e5W6LVvIukPda3R+9x2CRoxo4S4aGhcGmqA4T1lfXk0Xq5k48/lXZkKRCjXOmoBrXJ0upJSUf/QR+X98HlxqcIAhLpaQyy7za/LS0LgQOVVBoZUZP0uMCPefo3CuoxO6sy4kQE3Mi5g9G6Qk/5lnETYbXb/9Fp3V2vJgDQ2NgNEEhcZ5T/isWbjKygi55BJNSGhonAY0QaFx3iN0OmLuvvtsT0ND42eLVj1WQ0NDQ6NZNEGhoaGhodEsmqDQ0NDQ0GgWTVBoaGhoaDSLJig0NDQ0NJpFExQaGhoaGs2iCQoNDQ0NjWbRBIWGhoaGRrOcV7WehBBFwNHT/JhooLjFq84PtLWce/xc1gE/n7X8XNYB/teSJKVsc0Of80pQnAmEEJtPpXjWuYS2lnOPn8s64Oezlp/LOuD0rUUzPWloaGhoNIsmKDQ0NDQ0mkUTFN7MPdsTaEe0tZx7/FzWAT+ftfxc1gGnaS2aj0JDQ0NDo1k0jUJDQ0NDo1k0QaGhoaGh0SwXpKAQQnwkhNju+coUQmz3HL/hhOPbhRCKEGKgj/G/EELs8Zw/q2F17bCWSCHEd0KIdM+/EWd6DZ55+FyH51yqEGK952e+Swhh8TF+gOeaXUKIr4QQoWd0AU3ncqprGSiE2OAZv1kIMeyMLqBxHqe6Dr/jzzSnuhbPdfcKIQ54rvvLGZu89zxO9ffytBAi54R7TGnxoVLKC/oLeAV4ysfx/sARP2N6Az2B1UDa2V7DKa7lL8Bjnu8fA148l9aB2oVxJzDA8/8oQO9jzE/AWM/3twLPne11nMJalgGTPd9PAVafj+vwN/5sf7XxdzIOWA6YPf+PPdvrOIW1PA38rjXPuaBboQohBHAtMN7H6euAD32Nk1Lu84w/fZNrJW1dCzADuMTz/XxU4fdoO08vYHys4zJgp5RyB4CUssTP0J7A957vvwOWAk+exqm2yCmsRQLHNaIwIPd0zrMlTmEd/safNU5hLXcBL0gp7Z7rCk/3XFviVH8vreGCND2dwMVAgZQy3ce5Wfh/uZ6LtHUtcVLKPADPv7GnaX6BcvI6egBSCLFUCLFVCPGIn3G7geme738BdDrN8wyEtq7lfuAlIUQW8DLwf6d/qs3S1nX4G382aetaegAXCyE2CiHWCCGGnpHZNs+p/F5+I4TYKYR4JxBz889WoxBCLAfifZz6vZTyC8/3PnfaQojhQK2UcvdpnGLA/FzW0sZ1GIDRwFCgFlghhNgipVxx0j1uBf4hhHgK+BJwtOvkT+I0r+Uu4AEp5WdCiGuBt4EJ7boAD6d5HcdpTqNtN07zWgxABHCR59qPhRBdpMeW096c5rW8DjyHqrk+h2q+urXZCZ1tG9tZtO0ZgAKgo49zrwKPB3CP1ZwDPopTWQtwAEjwfJ8AHDiX1gHMBuad8P8ngYdbuE8PYNO59jsJdC1ABY05TgKoPB/X4W/8+bgW4FvgkhP+fxiIOR/XctJ9koHdLT3vQjY9TQD2SymzTzwohNChmi4WnpVZtY1TWcuXwBzP93OAL5q59nTjax1LgVQhhE0IYQDGAntPHiiEiPX8qwOeAP5zBubbHG1eC6pPYqzn+/HA2TTZnMo6/I0/W5zKWhbj8QUIIXoAJs5uxdlT+VtJOOG/M1HNts1ztqX8WZTI84A7fRy/BNjg4/hbeLQHzw83G7CjSvWl5/FaooAVqC+jFUDkObiOG4E9ng/0X/ys47fAQc/XC3h25OfpWkYDW4AdwEZgyPm4jubGn29rQRUM73mu2QqMP4/X8l9gF2qE1Jd4LArNfWklPDQ0NDQ0muVCNj1paGhoaASAJig0NDQ0NJpFExQaGhoaGs2iCQoNDQ0NjWbRBIWGhoaGRrNogkJDQ0NDo1k0QaGhoaGh0Sz/D9La52C55eEvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e8d4b939-2d62-4d7a-a866-7283b3e8e383",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  263227.0 people (VAP of 201461.0 ) and  133760.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "84c2cf09-b3ea-4fc2-a55b-62985882836b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAxsUlEQVR4nO3deXzU1bn48c8zk32HLARIIKwiyKJERFBUcAWX1rpAL9UuSmu1V22rP21v16tdrLbe3tpWq17rLmqtO7giIqIG2ffdhARIQgjZl5nn98dMwhAmEshsmTzv1yuv+W5nvs8kmWfOnO/5niOqijHGmOjlCHcAxhhjgssSvTHGRDlL9MYYE+Us0RtjTJSzRG+MMVEuJtwB+JOVlaUFBQXhDsMYY3qM5cuXV6hqtr99EZnoCwoKKCoqCncYxhjTY4jIrs72WdONMcZEOUv0xhgT5SzRG2NMlLNEb4wxUc4SvTHGRLkuJ3oRcYrIChF5zbs+XkQ+FpE1IvKqiKR1Uu5CEdkkIltF5I5ABW6MMaZrjqVGfzOwwWf9YeAOVR0LvATc1rGAiDiBB4CLgNHAHBEZffzhGmOMOVZd6kcvInnALOBu4IfezScAi73LbwMLgZ91KDoJ2Kqq273P8yxwGbC+e2H3Pp/t3M/SrZUAiIB4Hz3r4rNdjjjGd1vb8d5Fn+Pk0H7vjsPOgbQfe9i5fMo7HeAQwekQnCI4vI9Ox6Flh4BLFZdbcavidnvW3W71PCqeZe+6quLyHhOqIbXl6Id08wRBP0PwXwPBfxkSgldxxPvE5z0gPu+Bju8DOuz33Xdo2+HvKQUO/Qsr/dMTGZ+fEdwX6NXVG6buB24HUn22rQUuBV4GrgTy/ZQbCBT7rJcAp/k7gYjMA+YBDBo0qIth9R73LNjIZzurwh2GMSZAYp3Cul9dSFxM8C+VHjXRi8jFwD5VXS4iZ/vs+jbwZxH5OfAK0OyvuJ9tfqtlqvoQ8BBAYWGhzYbSgdMhnFrQh2fnnY6qttcO1PvrbKspdNzmOc673nZcF45RtP0v5btND9umh53Xt6buch++7PbWzN2qnhq+T81fxPP62rY7vOttxzhEcHi/LQS7jhfsf7xQfCnRoL8Kgv6LCkUC8P2fPuw9oEfub4vJ973HEft8ns/PexMOfXOYX1TMY0t34g7Rt9Su1OinApeKyEwgAUgTkSdVdS5wPoCIjMTTtNNRCYfX9POA0u6F3DtlJMaxcc9BKmubyElLCHc4xphuyE6ND+n5jvqdQVXvVNU8VS0AZgPvqepcEckBEBEH8F/A3/0U/wwYISJDRCTOW/6VgEXfiwzPSWFnZT2TfvMuD7y/leL99by8cjc3Pv05S7ZUhDs8Y8xxCMHlGqB7g5rNEZEbvcv/Av4PQEQGAA+r6kxVbRWRm/BcqHUCj6rqum5F3Etdf+ZQ8vsm8vjHu/jDwk38YeGm9n0pcTGcMSIrjNEZY45FWzNRKC44wzEmelVdBCzyLv8P8D9+jikFZvqsvwG80Z0gDaQnxXL1qYM4eVAfbn9hNSuLD7TvC1WtwBgTGG1N8z2hRm9CbOm2Cq7/55HDNyfF2Z/RmJ6k7RJsqOpoliF6iOqGFm55diX9MxJ56rrT6JeWQPH+erbsq2Hi4L7hDs8YcwwO1egjsOnGhM9dr62nsq6ZR649lX7eXjf5fZPI75sU5siMMceqretlqGr0NqhZhHlzTRm/fGUdm/bUtG9bsHYPzy8v4bvThjI2Lz2M0RljAsHa6Hu5J5btYum2Sh5bupPJQ/uyq7KesupGRuWm8p8zRoQ7PGNMALS30Yco01uNPsIkxDrJ65PIHReNYkdFHWXVjcwYlcO/vj+FhFhnuMMzxgRCiO6IbWM1+gjUJymO7501jOvPHMrK4ipG908nMc6SvDHRQgltt2hL9BFiV2UdzxeVULRzP0OyUwDPeC/Wo8aY6KMauguxYIk+rBqaXby5toznPivmkx37cQicfUION5w9LNyhGWOCKCQDz/mwRB9iqsqqkmrmFxXz6spSappaKchM4rYLTuBrp+SRm24DlhnTG4TqQixYog+ZytomXlqxm+eLSti0t4aEWAczx/bn6sJ8Jg3pG9I/ujEmvKzpJgq9vHI3P35+FS0uZUJ+Br/56lguGd+f1ITYcIdmjAkDuxgbhSprm2lxKU98ZxJnjsgOdzjGmDDz1OhDl+mtH30InDbU03OmorYpzJEYYyKBEtq2G0v0ITAqN420hBg+2b4/3KEYYyJBiNvoLdGHgNMhTBrSl092WKI3xoS+jd4SfYicNiSTHRV17DvYGO5QjDFh5nYrjhBmekv0IdLWTr/MavXG9HqKNd1EpdH900iJj+GT7ZXhDsUYE2aqWI0+GsU4HZxa0IdlluiN6fXcIb5jqsuJXkScIrJCRF7zrk8QkWUislJEikRkUifldorImrbjAhV4T3TWyGy2ldexymdib2NM7xSpNfqbgQ0+6/cAv1LVCcDPveudOUdVJ6hq4bGHGD2uKMwnNSGGf3y4PdyhGGPCyK0aeb1uRCQPmAU87LNZgTTvcjpQGtjQok9KfAxfP20Qb6wpo3h/fbjDMcaESaS20d8P3A64fbbdAvxBRIqBe4E7OymrwFsislxE5nV2AhGZ520CKiovL+9iWD3Pt6YMwSHCI0t2hDsUY0yYuFUjq9eNiFwM7FPV5R123QDcqqr5wK3AI508xVRVPQW4CLhRRKb5O0hVH1LVQlUtzM6O3vFgctMTuHTCAOYXFXOgvjnc4RhjwiASb5iaClwqIjuBZ4HpIvIkcC3wL+8xzwN+L8aqaqn3cR/wUmfH9Sbzpg2lvtnFHS+uoanVFe5wjDEhphphN0yp6p2qmqeqBcBs4D1VnYunTf4s72HTgS0dy4pIsoikti0D5wNrAxR7jzUqN42fXTyaBev2cP3jy2lxuY9eyBgTNdzu0LbRd2eY4uuB/xGRGKARmAcgIgOAh1V1JtAPeMk7qUYM8LSqLuheyNHhO2cM4eNtFbyzYR8lVQ0MyUoOd0jGmBBxq+KI1PHoVXURsMi7vASY6OeYUmCmd3k7ML67QUajpd4kf83pgy3JG9PLuDW0UwnanbFhUNPYwm3Pr2ZIVjJ3XDQq3OEYY0JMVXGEMPtaog+DX7+6nrLqBu67ajxJcf6/VDW3utlrI10aE5XcIb4Ya1MJhthb6/bw/PISbjpnOKcM6nPYvvKaJt7ftI/3Nuxjwbo9ABT917lkpcSHI1RjTJC4Q3zDlCX6EKqsbeInL61hdP80/nPGCFSVDWU1vLNhL+9u3Nc+Bk5uWgLJcU7qml3UNLZaojcmyoR6CARL9CGiqvzkpTVU1DbzvbOGcdfr63ln/V5KqxsRgQn5GfzovJFMPzGH0f3TeHllKbc8tzLcYRtjgiDUQyBYog+R5z4rZuG6vQDc9foGEmIdnDkim1vOHck5o3LITrVauzG9RUR3rzTHb/PeWgCuKszjgjG5TB2eRUKs86jlVDXYoRljQswuxkapn18ymp9fMjrcYRhjIoD1ozdAaAc8MsaEloa46cYSvTHGhJjLHWGDmpnganW5aWyxESyN6U3cCo4QVumtjT5M1pce5MlPdvH0J1+QnRrPpz+Z4bfNzi7FGhN9rNdND6Cq7K9rJvM4b2R6Z/1ebnhqeftXt/KapkCGZ4yJcKHudWNNN8dodckBhtz5BhPveocfPLMCl/vY6twL1u7h+099zugB6Xzykxnk9Um0C6/G9DJuNzgt0Ucu30HIXl1VyptrywBPLf/ehZs48573eGvdHpbvqqKkqp4dFXW8vHI3H22t4M/vbuH7Ty1nzMA0Hv/WJDKS4pgzaRCqcPOzK6mstZq9Mb2By4ZAiGzDc1IoHNyHol1VANz54hoGZCSyaFM5f3l/KwDznug4ve4hs8b1594rxpMY57lZ6vtnD6PVpfzve1uorGviqesmA1BR65lP9o9vb2bW2P7MHNs/mC/LGBNCqkqMM3T1bEv0xyEp/tCvraaplcv/uhSAKcMyueeKcbyzfi9ZqfGUVDUQH+NgeE4KTofQNzmOUblphz2XiHDzuSNIiHXw2zc38vCH23lr/V4+3bEfgNdXl/H66jJevekMxualh+5FGmOCxuVW4mOs103E2lB2kMWbyxmRk8ILN0zh3Q17yUiKJSc1gRNyU4l1Ovjm1CHH/LwT8jMAzzg48TEObrvgBPqlJfDj51cBsL++OZAvwxgTRta9MsLNLyoG4B/XFJKeGMvlp+QF5Hn7Jse1L581MpsbzxmO263tib6msSUg5zHGhF/E3hkrIk4RWSEir3nXJ4jIMhFZKSJFIjKpk3IXisgmEdkqIncEKvBwaGp18dKK3UwZlklBgOd5zUg6lOg37DkIeD7xf33ZGK4uzG+v8Rtjej5XBHevvBnY4LN+D/ArVZ0A/Ny7fhgRcQIPABcBo4E5ItJjR/ZavLmCA/UtnFrQN+DPnZ0az9PXnwZA8f6G9m6b15xewO+vGEden6SAn9MYEx5ud2jHo+9SoheRPGAW8LDPZgXariymA6V+ik4CtqrqdlVtBp4FLjv+cMOrqdUzVMHQ7MDW5ttMGZbFLy8ZzXfPGhrSr3XGmNCK1Dtj7wduB1J9tt0CLBSRe/F8YEzxU24gUOyzXgKcdsxRRojl3i6VYwYEr/fL8VzINcb0LG5VnCHM9Eet0YvIxcA+Ve3YOfwG4FZVzQduBR7xV9zPNr+3korIPG9bf1F5efnRwgqLVpcndBuEzBjTHZE4euVU4FIR2Ymn6WW6iDwJXAv8y3vM83iaaToqAfJ91vPw38SDqj6kqoWqWpidnd3F8EPrpunDSUuI4eoHP+axj3aEOxxjTA+lIe5eedREr6p3qmqeqhYAs4H3VHUunoR9lvew6cAWP8U/A0aIyBARifOWfyUgkYdBv7QE/j53InXNLn756noe/3hnuEMyxvRAoW6j7849uNcD94nIKuA3wDwAERkgIm8AqGorcBOwEE+Pnfmquq57IYfXlOFZrPrF+Zw8KIOfv7yOC+9fjPsYBzYzxvRukdy9ElVdpKoXe5eXqOpEVR2vqqe1teGraqmqzvQp84aqjlTVYap6d2DDD4/0xFjmf/d0Zo7NZeOeGp76ZFe4QzLG9CCqoZ0u1EavPE6xTgcPfP0Uxudn8Me3N1PX1BrukIwxPUkIGwJsCIRuEBG+PbWAm59dyZhfLOTicf0RkfauRiL4LB/ajkDbmu8xja1uclLjufW8kaTE25/GmGglEtrZ4yybdNMl4wbw7oZ9fLpjP+tLD7b/8VTVZxna1lQ9P/gcB54/ell1IwBPLtvFH64cz6XjB4TmRRhjQkr89jwPHkv03eRwCH+ec3LAnu/DLeV874nl3P/OZkv0xkQx1dDV6a2NPsKcOSKbSycMYHt5HY8usb76xkSjUDfdWKKPQOPzMgB4YtkuVnxRxdhfLmT+Z8VfXsgY02MIhzfhBpsl+gh0x7/WALCjoo6v/nUpNY2t3P7iam5+dgUNzTb8gjE9nYSybyWW6CPSizecfsS204b05eWVpXzlgY+sK6cxUcB63fRyEwf3ZefvZuF2K4s272NETir5fZN4vqiY215YzaJN5cwaZ5OFG9NTeZpuQpfqLdFHMIdDmD6qX/t6v7QEAJLinOEKyRgTCHYx1nTm8Y93kp4Yy+ShmeEOxRjTDaGeV8gSfQ+xdnc172zYx3VnDCHRavTG9HzW68Z09Jf3tgJw7dSC8AZijOk2EUFDmOkt0fcAqsqCdXsA+KKyPszRGGO6K9T96O1ibIQr3l/PfW9tAuDcE3MYlZt6lBLGmEgnYoneeL28cjc/mr8KEbh5xghuOXdEyG+0MMYEng1q1oOsLz3IY0t34DvBVHOrm1UlB8hMjsM7lzjCoeGI24Yrdng3HNonnkefIYw/2VHJmAFpPPiNQnLTE0L86owxwRTKNnpL9N3w8qrdzC8qYWBGYvs2l1tpanXhECG/bxLgMxSxd7jitqGK3d6hjNUNitu7/9AQx6cPy+LXl46xJG9MlLGmmx4mPsbBR3dMD3cYxpgexm6YMsaYKCYikVmjFxEnUATsVtWLReQ54ATv7gzggKpO8FNuJ1ADuIBWVS3sZszGGNOjea7CRWYb/c3ABiANQFWvbtshIvcB1V9S9hxVrTiuCANsW3kt33tiOVv21QJw4Zjc9tnYO3Zo8Xtl3GfT66vLQjqTuzEmOkRkG72I5AGzgLuBH3bYJ8BVQI9oqN5ZUdee5IH2G5EARuSktC/7+xt0HG1uaFYyYwamBzxGY0x0C3UFsas1+vuB2wF/d+ucCexV1S2dlFXgLRFR4EFVfcjfQSIyD5gHMGjQoC6Gdewqa5sPWx+cmcQu792mL35/CmkJsUE7tzHGtImoi7EicjGwT1WXd3LIHOCZL3mKqap6CnARcKOITPN3kKo+pKqFqlqYnZ19tLCO24GGwxP9Lp8hBUoPNATtvMYY00aQiBuPfipwqYjMBBKANBF5UlXnikgMcDkwsbPCqlrqfdwnIi8Bk4DF3Q/9+MybNoxJQzJZu7uahFgnr64q5YPN5XxzSgEn9LPhBYwxwRdxk4Or6p2qmqeqBcBs4D1VnevdfS6wUVVL/JUVkWQRSW1bBs4H1gYk8m6YkJ/B3MmDWbu7mg82l/OLS0bzy0vH2PACxpiQ6GmTg8+mQ7ONiAwQkTe8q/2AJSKyCvgUeF1VF3TznAHxyJIdPLZ0J9+eOoRvTR0S7nCMMb2JSOTOGauqi4BFPuvf9HNMKTDTu7wdGN+dAINhwdoy7np9PReOyeWns04MdzjGmF4m1HPG9ro7Yz//ooqbn13JhPwM7p89AafDmmuMMaEV6lbiXpXoN5Qd5Fv/9xm56Qk8fE0hCbE2JZ8xJvR6Wht9j7Grso5vPPIpibFOnvzOaWSmxIc7JGNML2VTCQZBZW0T1zz6KS63myevm9Q+fLAxxoRDqBuMo36Y4sYWF/OeWM6e6kaemTeZ4TnWV94YE37WdBMgqsrVD37M8l1VfGPyYE4Z1CfcIRljTMgHNYvqRP/whztYVeIZVPO5ouIwR2OMMR6CtdEHTN/kOMblpTNtZDY1ja28vros3CEZYwxYjT5wvjYxj1duOoP/OM0zGubvFmwIc0TGGOPtXhnC80V1om/TNlhZ8X4bndIYE34S4kzfKxJ9fKznZV5dmB/mSIwxxtrog6KtLaxvSlx4AzHGGKzXTVBkJMWSFOfk5RW7qWtqDXc4xpheLuLGo48GsU4H9c0uSqsb2XuwMdzhGGN6uVDPMNUrEv0Vf/+4fXlodsqXHGmMMcFnNfoAq29uZVXxAQCenTc5vMEYY4yXtdEH0Nvr9wIwaUhfJg/NDHM0xhjTNnpl6ER9or/52ZUA/PGqiJvoyhjTSwmEtEof9Ym+zYvLd4c7BGOMAayNPuC+O20oAH96Z3OYIzHGGI+InWFKRJwiskJEXvOuPyciK70/O0VkZSflLhSRTSKyVUTuCFDcnappbKGmsYWGZhdNrZ4ulQAf3HZ2sE9tjDFdEuoZpo5l4pGbgQ1AGoCqXt22Q0TuA6o7FhARJ/AAcB5QAnwmIq+o6vruBN2Zl1aUcOtzq47YPnFwHwZnJgfjlMYYc8xCXaPvUqIXkTxgFnA38MMO+wS4Cpjup+gkYKuqbvce+yxwGRCURL95by0AP515Ii5VXG6l1aWcMcJ62xhjIkeoh0Doao3+fuB2wN88fGcCe1V1i599AwHfGT9KgNP8nUBE5gHzAAYNGtTFsA5XVdcMwDVTBhMf4zyu5zDGmOCLsO6VInIxsE9Vl3dyyBzgmc6K+9nm9/Wp6kOqWqiqhdnZ2UcLy6++yXE4BOKcUX+N2RjTg3lq9JHVRj8VuFREZgIJQJqIPKmqc0UkBrgcmNhJ2RLAd2zgPKC0OwF/mb0Hm8hOjcfTmmSMMZEp1BnqqFVfVb1TVfNUtQCYDbynqnO9u88FNqpqSSfFPwNGiMgQEYnzln8lAHH79cHmcvYebKKytilYpzDGmG7racMUz6ZDs42IDBCRNwBUtRW4CViIp8fOfFVd181zdmrGqBwAJt71DtUNLcE6jTHGdEtETzyiqotU9WKf9W+q6t87HFOqqjN91t9Q1ZGqOkxV7+5+yJ37/RXjOGN4FgCPfbQzmKcyxpjj1tNq9BHnyetOY+LgPry5tizcoRhjjF82BEIAzBrbn417athRURfuUIwx5gg28UgAnDe6HwCLN5eHORJjjPEjxN1uojLR5/VJJCsljjW7jxiVwRhjIkIom26OZaybiFff3MpnO6twu5WK2maW76oKd0jGGHMEz3j0oTtfVCX6hxZv5/53Do3EUNNoXSyNMZEn1DNMRVWij/UOffDsvMkkxDoZkJ4Q5oiMMeZIntErI2sIhB4jKc4zkNkJ/VLpkxwX5miMMcY/617ZDf3TEwHYVl4b5kiMMaZzETvDVE8waUhfAD7ZsT/MkRhjTOdCPcNUVCX6vslxjOyXwqeW6I0xEcxq9N00aUhfinbup9XlDncoxhjjn4110z2jctOoa3axt8aGKjbGRCYJ8a2xUZfoD3r7zqcnxoY5EmOM8S/UM0xFXaLPSfX0nS+3Gr0xJkIJ1r2yW7JT4wFsliljTMSy8ei7KdN7o1RFbXOYIzHGGP8ieoapniA1wXOzb11Ta5gjMcYY/6xG301tF2EP2JyxxpgIFbFDIIiIU0RWiMhrPtt+ICKbRGSdiNzTSbmdIrJGRFaKSFEggv4yaQmxOAQO1FvTjTEmUklIa/THMqjZzcAGIA1ARM4BLgPGqWqTiOR8SdlzVLXi+MPsOodDyEiKY3+dJXpjTGSSEA9I36UavYjkAbOAh3023wD8TlWbAFR1X+DDOz4ZSbFUWY3eGBOhInUIhPuB2wHfcQVGAmeKyCci8oGInNpJWQXeEpHlIjKvsxOIyDwRKRKRovLy7s31mhwXQ0Ozq1vPYYwxwRJxbfQicjGwT1WXd9gVA/QBJgO3AfNFxN99vVNV9RTgIuBGEZnm7zyq+pCqFqpqYXZ29jG9iI5inUKLK5S/RmOM6TpBIm7ikanApSIyE0gA0kTkSaAE+Jd6ov1URNxAFnBYdVxVS72P+0TkJWASsDiAr+EIMU4HLTaomTEmQkVcjV5V71TVPFUtAGYD76nqXODfwHQAERkJxAGHXXAVkWQRSW1bBs4H1gbyBfgT6xRa3VajN8ZEpkhto/fnUWCoiKwFngWuVVUVkQEi8ob3mH7AEhFZBXwKvK6qC7oX8tHFOKxGb4yJXCKR13TTTlUXAYu8y83AXD/HlAIzvcvbgfHdDfJYOR2Cy2r0xpgIta+mkdSE0I2wG3V3xgI0NLtIjHWGOwxjjDlCi8vNki0VTBmWGbJzRmWir29xkRR/TF9WjDEmJP709mYONrYya1z/kJ0z6hK9qrK7qp7slPhwh2KMMUfYvLeW+BgHLS4NWTt91CX6L/bXU1HbzCmDM8IdijHGHGHOpHxiHML1jxexYO2ekJwz6hL9519UAXDKoD5hjsQYY44048R+LP/ZeQC8uXYPjS3Bv4s/6hL9B5vK6ZMUy8h+qe3b/vT2Zib+99u8uaYsjJEZY4xHQqyTb04p4JVVpcx9+JOgny/qEv1H2yqZNjIbp+PQaAwfbC6nsq6ZW+ev5IXlJbiP0vWyrqmVrftqjtp+tvtAA2+sKWNl8YFAhG6M6UV+cclopo3MpmhXVdDb6qOqa4rbrVTUNvHyylKWbqukqcVFY6ub5lbPzVN9kuL48fOr+GBzOfdeOY74mENdMBuaXawuOUDRrioeWbKD/XXNjM9L54rCfHZW1LGrsp60hBhSE2KobXKxrbyWVSUH2u9u+9bUAn42azQOh7/hfowx5nAiwpRhmSzeXM7fPtjG988eHrRzRVWidziEn848kXWlB0mIdRAf4yQ+1kFCjJNpI7M4ZVAf/v7Bdn6/YCNlBxo4Z1QOqQkxLNteyfsby2nwtpWdOSKLM0dk8fQnX/Czf68lLsbBkMxk6ppbOdjQQlJcDIMyk7h5xghmjOrHi5+X8H8f7SQzOY6bpo8I82/BGNNTfHfaUD7ZXskjH+7ghrOG4X9cyO6LqkQPcN2ZQ790/w1nD6N/egK/fm09RQs3AZCVEs/lpwxkxok5nJCbxsCMRAC+c8ZQtpXXMqhvEglfcgPWSQPTOFDfzH1vb2bWuAEMyUoO3AsyxkQtEWHayGze31ROeW0TOakJQTlP1CX6rvjKyQP5yskDaWxxUVXfTE5qwmFt+m2cDjnsom5nRISLxw3g3ytLqWm0uWqNMV1X7Z3fOsYRvEumvTLRt0mIddI/PTEgz1XX3ApA8lHuyHW7ldfXlPH66jJSE2IYnJnE3MmDyUiKC0gcxpiepe0a4uLN5Xzl5IFBOUfU9boJlzin51f5fFGJ3yvoqsqCtXuY+ecP+cEzK1hVcoAPt1Rw71ubOfePi3l9dVlIR7MzxkSG/5wxgklD+nLbC6tYtr0yKOfo1TX6QDp/TC5zJuXz9w+2sbOijisL80iOjyEh1klVXTP3v7uFVcUHGJqVzP/MnsDF4wbgdAjrSw/y/15czY1Pf875o/vx28vHkmnDNxjTayTEOrnzolF89a9LeXF5CZOHBn6wM0v0AeJ0CL/56lgGpCfyl/e3smDd4bc256TGc88V47j85IHEOA99kRo9II2Xvj+FR5bs4L63NnP2vYu44exhQb0Cb4yJLJ/u2A/A7EmDgvL8lugDSET4wYwRXH1qPsVVDTS1uGhqdaMopw3J7LT9Psbp4LtnDePsE3L4w8JN3LNgE8X7G7jrKyf5vUhsjIkeLrfyyJIdDMlKZkJ+RlDOYYk+CHLSEshJO/ZuUifkpvKPaybyh4Wb+OuibawsPsDtF5zA2SdkW+3emCjkdisPLt7Gvpom7jn/hKBV7OxibIQREW6/cBQPfP0U6ptb+dZjnzH7oWUctG6bxkSd9zft454Fnvt5gjk+vSX6CDVrXH/e+eFZ/PdlY/hkx37++NbmcIdkjAmwgqzk9h57723cF7TzWKKPYLFOB984vYDCwX14bOlOmwfXmCgzLDuFj+6YzqjcVP787pagnafLiV5EnCKyQkRe89n2AxHZJCLrROSeTspd6D1mq4jcEYige5s53ivxK7xj7Rtjokd2ajyXThjAln21rArSSLjHUqO/GdjQtiIi5wCXAeNUdQxwb8cCIuIEHgAuAkYDc0RkdLci7oWmj8ohNy2BW+evpKk1+JMUGGNC68IxuQC8tT44M051qdeNiOQBs4C7gR96N98A/E5VmwBU1V8D0yRgq6pu9z7Ps3g+HNZ3M+5epU9yHL+/YhzXPvopL68s5arC/HCHZHqppVsruP3F1dQ1teJWcKui3kfPD0fc4S2I74rvg2dZjjxWjizi3S5HbPPz9Icf24XnEhFEwCGeGBxyaJtnuycyhwh412McQkKsE4dATWMriqe8Q8RTFnA4Oqx79x96Hk8TbVs7/cJ1e7ntglEEWle7V94P3A74jvA1EjhTRO4GGoEfq+pnHcoNBIp91kuA0/ydQETmAfMABg0Kzk0DPdm0EVmMyk3lb4u2MXNsf1KOMqaOMYG2rrSaHzyzgtSEGC4ZP8CbwNoSl09Ck0MJ1Tflt+V/9d3qZ9H3g0L97vfd5v/YQ9t89ncSi6pnn/p8aHkeffa3fYjRtl9pcSlNrW5cbjf90hJwiHT4wGsrp+0finjLu92eR5dbaWptpcXlGe9m0pC+R76IADhqthCRi4F9qrpcRM7uULYPMBk4FZgvIkP18I9zf51C/V5RVNWHgIcACgsL7apjByLCzy4ezTWPfsrtL6zir/8xMdwhmV5iXWk1f3p7M+9u3Ed2SjwPX3sqw3NSwh2WOQZdqRZOBS4VkZlAApAmIk/iqZ3/y5vYPxURN5AFlPuULQF82xnygNKARN4LTR2exXVnDOGhD7dT29RqtXpz3FSVPQcb2V5ex87KOnJSEzh5UAZZPuMsbd1Xy9OffME/P95JWkIMN549nG+fMYS+yTbSak9z1EyhqncCdwJ4a/Q/VtW5IvI9YDqwSERGAnFARYfinwEjRGQIsBuYDXw9YNH3QhMH90EXw+a9NZwyqE+4wzERZHXJAV5fXUZ6UixzJw8mLSEWgNqmVnZXNVBR20RFbRNrSqp5e8NedlXWH/Ec/dMTSI6PYW91IzVNrTgErpyYz09mnkh6UmyoX5IJkO5UCR8FHhWRtUAzcK2qqogMAB5W1Zmq2ioiNwELASfwqKqu637Yvdf+umYA+tr49cbHgx9s47dvbiTWKbS4lL8t2sakgr7EOIX3N5bT7G0DBs+Q2qcPy+SbUwoY2S+VwZlJlFU3suKLKjaU1dDQ7OKM4VkMy07mgjG5xzWch4ksx5ToVXURsMi73AzM9XNMKTDTZ/0N4I3uBGkOGZzpmaZw9e5qCmzKQgO8vHI3v31zI5eMH8DdXz2JLyrreWTJDjaUHeRgQwtzJuVTWNCX7NR4slLiGZCRQFLc4W/9vD5JnFoQnAuBJvyskbeHmTSkL4Mzk3ho8TYuGdffBjvr5XZV1vHTl9ZSOLgPf7pqPDFOBycNTOdPV08Id2gmglii72GcDuHa0wv49WvrKalqIL9vUrhDMiFwsLGFsgONNLS4KKmqp6Sqgd1VDXy4pRyHwP2zJxw2z4ExvizR90Bt/XHjY+yNHa2q61so2rWfZdsrWbZ9P+tKq+k41FF6Yiz5fRP51WUnkdfHPvBN5yzR90AfbC5nRE6KXSSLItUNLTy6ZAdvrd9LSVU9NY2eyebjYhycnJ/BD6aPYHhOComxTvL6JjIwI5HUBOsFY7rGEn0PdLCxlYRYJ6pqbfQ9XG1TK499tIOHFm/nYGMrpw/N5NSCgQzMSGTswHROGdyHhFhnuMM0PZwl+h7oiol5/Ozfa3l+eYmNe9MD7a9rZvmuKpZtr+TFz0s4UN/CuSfmcMu5IzlpYHq4wzNRyBJ9D/QfkwbxfFEx/1i83RJ9D7K65AC/e3MjS7dVAhDrFKaPyuGGs4cHba5QY8ASfY/kcAgXndSf3y/YSHVDC+mJ1lYbDHVNrbyzYS8HG1vpkxTLtJHZ7XebHqs1JdVc9eDHpMTH8MPzRjJ5aCbj8tKtWcaEhCX6Hio33TMmSXlNU49P9K0uN+vLDpKeGMugvkkRcd1h+a4qbn1uJV/sPzRMQJzTwW8uH8sVE/O69ByqysHGVnZW1PHdJ5aTmRzPv2+cSnZq/NELGxNAluh7qOL9DYgQsUmjqdWF2w2JcYfXWFWVhhYXB+pbWLKlgg82l/PhlnIOenuZ5PVJ5L4rx3Pa0MzDyrW43MQ4JOgfAnsPNvLUsl08sGgbuWkJPP7tSYzKTaW4qp773trM7S+swumAr558ZLKvrm/h8y+qWLK1gmXbK9lVWU9tk+d1JcU5eeF7UyL272WimyX6HmpV8QGGZ6dEXG1+yZYKnisq5r0Ne2locTE8J4WxAzMY0S+FPdWNLFy3h7Lqxvbj+6XFc+FJuUwdnkVtUyv/WLydOf9YxmlDMjlzZBblNU38e8VuqupbyEyO44KTcrnzolEB7VrociuLNu3jmU+LeX/TPlxuZdbY/vz2a2Pbm2py0hJ45NpT+fZjn3Hrc6v4eFsl2anxVNY2U1HbxOa9te21/7gYB4WD+3DFxDwGZiQysE8i4/LSra+7CRvpOBtMJCgsLNSioqJwhxHRZty3iOE5KTz4jcJwhwJ4auoPLt7O797cSN/kOC4Y04/slHjW7K5mze5qKmqbiYtxcNbIbE4elEFyXAyThvRlVG7qYbX02qZWHvxgGwvX7WHz3lpiHMKFJ+UyIieVHRW1vLKqlJT4GM4ZlcP0UTnMOLHfcQ/XXFJVz/yiEp4vKqasupGslHiumJjH7FPzOx1HqLHFxX+/tp6XVuymqdVNZnIcmSnxDMlK4qSB6UzIy7AukSYsRGS5qvpNCJboe6irH/yY6oYWFtwyLdyhAPDuhr18559FnHtiDg/8xynExxxKdKpKXbOLhBjHMd2mX93QggiHXQBdWXyAJz7exfub9rG/rpkxA9J4+capfp9XVdvH7d9VWU9VfTMnD+pDfXMrtz2/mjfWlgEwbUQ2cyblM+PEfsR2Mb5Wl9szs5Ij/NcTjIEvT/TWdNNDnTe6H3e9voHr/lnEDWcPY+Lg8IxNv3Z3NU98vIvX15SREh/DfVdOOCzJg2d2rOOpdftrlpqQn8GE/AxcbuXF5SXc/uJq/rVid3s30y8q6/nh/JWsLqnGrUqrW0mJj2lvK79gTD/Ka5pYWXyAG84axtdPG3RcTSo2rozpSSzR91DfnFJAU6ubBz/Yxjsb9vLNKQXccu4IMkI4Tv0Hm8v50fyVVNQ2M3NsLv85Y0TIJqdwOoQrC/P4w1ub+HTHfq4qzKesuoGvP7yMgw0tXHP6YGJjHKQnxlK8v54hWcnUNbl4YNFWYhzCn+eczMXjBoQkVmPCzZpuerj65lbuWbCJx5buJCHWwXmjc8lNi2dETiqnD8sM+OiW60qrefjDHXy4pZyK2mYKMpN48BuFnJCbevTCQXDBnxZTVd/MlYV5PPD+NgBeuWkq4/Iy/B7f2OIi1unAaU0uJspYG30vsHHPQf65dCfvbyznQEMzjS2eGYWG56Qw86RcLhrb/4gLn13R6nJzsLEVtyr/++4Wnli2i+T4GM47sR+nDO7DlYV5RzTVhNL8z4r5yUtraHUrmclx3HHRKK60u4VNL2SJvpdRVbbsq+WjrRUsXLeHT3fsx60wdmA6/zXrRLJS41mwdg+lBxo4Y3gWF4zJPeKi4q7KOn716nqWba+kvtkFgENg7uTB/Oi8EyJq/tDGFhdOh3T5Qqox0cgSfS9XXtPEgnV7+Mt7W9h7sKl9e0Ksg8YWNz86byQ3TR+OiFB6oIHHP97FU8t24XAIX5kwgMGZyTS73Jw5IosxA2zQLWMiUUASvYg4gSJgt6peLCK/BK4Hyr2H/MQ7P2zHcjuBGsAFtHYWiC9L9MFR39zKa6vLQOH0YZkMyEjkludW8uqqUtITY0lNiKGsuhFV5fzRufx01ok2g5UxPUSgulfeDGwA0ny2/UlV7+1C2XNUteIYzmWCICku5ojRLu/52jhOLejDpj01NDS76JeewNcnDbIEb0wU6VKiF5E8YBZwN/DDoEZkQioxzsk1pxeEOwxjTBB19erV/cDtgLvD9ptEZLWIPCoind2xo8BbIrJcROZ1dgIRmSciRSJSVF5e3tlhxhhjjtFRE72IXAzsU9XlHXb9DRgGTADKgPs6eYqpqnoKcBFwo4j4vWdfVR9S1UJVLczOzu5q/MYYY46iKzX6qcCl3ouqzwLTReRJVd2rqi5VdQP/ACb5K6yqpd7HfcBLnR1njDEmOI6a6FX1TlXNU9UCYDbwnqrOFZH+Pod9FVjbsayIJItIatsycL6/44wxxgRPd8a6uUdEJuBpg98JfBdARAYAD6vqTKAf8JL3bswY4GlVXdCdgI0xxhybY0r0qroIWORd/kYnx5QCM73L24Hx3YrQGGNMt9g948YYE+Us0RtjTJSLyLFuRKQc2BWkp88CIv0u3Z4QI1icgWZxBlZvi3Owqvrtmx6RiT6YRKSoK+PthFNPiBEszkCzOAPL4jzEmm6MMSbKWaI3xpgo1xsT/UPhDqALekKMYHEGmsUZWBanV69rozfGmN6mN9bojTGmV7FEb4wxUS5qE72IXCki60TELSKFPtvP846Nv8b7ON1P2VdEJCSDrx1PnCKyQERWecv93TvNY0TFKSJJIvK6iGz0lvtdsGM8nji9++4WkWIRqY3gGCd6t28VkT+LdwCpMMWZKSLvi0itiPylQ5mrvXNUrBORe4IdYzfinOP9fa72vp+yIi1OEUkVkZU+PxUicv9xnVxVo/IHOBE4Ac/YPIU+208GBniXT8IzB65vucuBp4G1kRonkOZ9FOBFYHakxQkk4ZlCEiAO+BC4KNLi9K5PBvoDtRH8N/8UON37N38zzL/LZOAM4HvAX3y2ZwJfANne9X8CMyIwzhhgH5DlXb8H+GWkxemn/HJg2vGcuzujV0Y0Vd0A0LHio6orfFbXAQkiEq+qTSKSgmeqxHnA/EiNU1UPerfH4EmiQb+ifhxx1gPve49pFpHPgbwIjLNJVZf5KxMpMQJ98Xy4f+wt9zjwFTwJPxxx1gFLRGR4hyJDgc2q2jZF3DvA14B3IyxO8f4ki0glnnmwtwYzxuOMs52IjABy8FSYjlnUNt100deAFara5F3/bzwzZdWHLyS/OsaJiCzEUyupAV4IV2AdHBEngIhkAJcQ5Df8MfAbZ4TxjXEgUOKzr8S7LdJsBUaJSIGIxOD5MMr/8iKhp6otwA3AGqAUGA08Etagjm4O8Jx6q/bHqkfX6EXkHSDXz66fqurLRyk7Bvg9nslQEM/Y+sNV9VYRKYjUONuo6gUikgA8BUwH3o7EOL1v+GeAP6tn2OpuC0acgRbgGP191QjIt7juxHlEQKpVInID8Bye+aWX4qnld1sg4xSRWDyJ/mRgO/C/wJ3AXZEUZwezAb9Dw3dFj070qnru8ZQTkTw80xpeo6rbvJtPByaKZ8rEGCBHRBap6tkRFqfv8zaKyCvAZQQg0QcpzoeALap6fzfDaxes32cgBTjGEg5v9srDUxPttuON80ue71XgVQARmQe4AvS8gYxzgvc5twGIyHzgjkA8caB/nwAiMh6I0SPn7e6yXtd0421GeB24U1U/atuuqn9T1QHqmTLxDDxtjWeHJUg6j1NEUsQ7jaO3tjwT2BiWIOk8Tu++u4B04JbQR3a4L4szUnzJ/2YZUCMik729ba4BulM7DBoRyfE+9gG+Dzwc3oj82g2MFpG2kR7PAzaEMZ6jmYPnW/HxC/aV5nD94JnHtgRoAvYCC73b/wuoA1b6/OR0KFtA6HrdHFOceKZn/AxYjeeC3f/i+bSPtDjz8DQvbPDZfl2kxendd4+3jNv7+MsIjLEQz3zL24C/4L2rPRxxevftBPYDtd5jRnu3PwOs9/4EvTdYN+L8nvd/czWebyCZkRind992YFR3zm1DIBhjTJTrdU03xhjT21iiN8aYKGeJ3hhjopwlemOMiXKW6I0xJspZojfGmChnid4YY6Lc/wdCouqMDARkmAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rebuild tractMAP, excluding skipped / sliced tracts.  #NOT NEEDED FOR OHIO -- NO SLICED populous TRACTS\n",
    "counter = -1 #Watchout - low-number tracts may have been skipped\n",
    "found = \"no\"\n",
    "while found == \"no\":\n",
    "    counter +=1\n",
    "    if isSkippedTract[counter]==0:\n",
    "        starter = counter\n",
    "        found = \"yes\"\n",
    "\n",
    "tractMAP = tractGeom[starter]\n",
    "for t in range(starter+1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "\n",
    "if type(tractMAP) != type(tractGeom[1]):\n",
    "    for geom in tractMAP.geoms:\n",
    "        x,y = geom.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "else:\n",
    "    x,y = tractMAP.exterior.xy\n",
    "    plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d4538dfc-7d35-46b4-95c1-c5d93169e971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gap = Point(-123.4,47.7)\n",
    "for t in range(nTracts):\n",
    "    if tractGeom[t].intersects(gap):\n",
    "        plotPoly(tractGeom[t])\n",
    "        plt.text(tractGeom[t].centroid.x,tractGeom[t].centroid.y,t)\n",
    "for p in range(nPrecincts):\n",
    "    if vtdGeom[p].intersects(gap):\n",
    "        plotPoly(vtdGeom[p])\n",
    "        print\n",
    "\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c85e4765-4565-4d98-881a-87993be82e85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAA1CklEQVR4nO3deXxV1bnw8d9zTuZ5ZgoQZmRWAog4IM6Is1W0TrUVtdpr67W21l7b26pva20vta32UmurVXGeqlxxIs7IPA8S5iRCEgiEzMk5z/vHOQkhnJDpTAnP9wOf7GGtvZ+T4TnrrL33WqKqGGOM6bkcoQ7AGGNMYFmiN8aYHs4SvTHG9HCW6I0xpoezRG+MMT1cRKgD8CUjI0NzcnJCHYYxxnQby5cvL1XVTF/7wjLR5+TksGzZslCHYYwx3YaI7Gxtn3XdGGNMD2eJ3hhjejhL9MYY08NZojfGmB7OEr0xxvRw7U70IuIUkZUi8rZ3fbyIfCkia0Xk3yKS1Eq980Vks4jki8hP/RW4McaY9ulIi/4uYGOz9SeBn6rqWOB14MctK4iIE/gLcAEwCrhGREZ1PlxjjDEd1a776EUkG7gQeAi427t5BPCJd/l9YCHwXy2qTgbyVXWb9zgvAJcAG7oWtmmvL7aWsnR7GQAiII1fRZrKeLbLEfvh8DZPGWmxz7vNW6dxh/g4XvNzeP/hEMHpEBwOwSmC0wFOhwOn4/A+pwguVVxuxa2Kyw1uVdxuPXp7i22BJm0X6cLBA3f0QMYdwLA9xw9g9Ef8bTT7PfXsk2Z/N239XbT8Ozry70ABz6+nEhPp5NShGUQ4A9+D3t4HpuYC9wKJzbatAy4G3gS+BfT3Ua8fsLvZegEwxdcJRGQOMAdgwIAB7QzLtOXhBRtZV1ge6jCMMT48c/NkTh/u82FWv2oz0YvILKBYVZeLyPRmu24GHhORB4C3gDpf1X1s89ncUtV5wDyA3Nxcmw3FT5wOB6cNy+AfN00CDrcoFKV5w7f5Nk8ZPaI8Leo0ljncQvHsR488x+Fje8t7y7pbtMgblxvch7e73YrDIUe08B3NWvwttx9eDmTbr5VfYH8dO4AH10BGHuC/2EB/z1v+7rf1t9G8ZX7k30Szsj7+zho/EWwtqeAH81dSU+8K4Cs7rD0t+mnAxSIyE4gBkkTkWVW9DjgXQESG4+naaamAI1v62UBR10I2HZEcG0lxeQ0Hq+tJT4gOdTjGGDwNGziyCzWQ2uwcUtX7VDVbVXOA2cBHqnqdiGQBiIgD+DnwVx/VlwLDRGSQiER567/lt+hNm4ZkxrNpzyGmPPwhLy3dzd7yGh5duJm7X1pFeU19qMMz5rjU+OkqOGm+a4OaXSMid3iXXwP+ASAifYEnVXWmqjaIyJ14LtQ6gadUdX2XIjYdcueZQxmQFsfTX+zg3lfXHLHv6tz+TBmcHqLIjDl+NXbnBKlB37FEr6p5QJ53+Y/AH32UKQJmNltfACzoSpCm89ITovnOtEGM6JXIPS+vpuhgTdO+YH1sNMYcqbHbPlh/gvZk7HHg7TVFXP/UEupc7iO2x0U5QxSRMce3xpsdAnvbwGFhOR698Z/i8hrue3Ut47OTefrmySRER7C64CA19S5G9/X5MLMxJsCabsQJx64b0/386u0N1Da4efRb40mMiQRgQv+U0AZlzHGuqY8+SOezrpseYntpJT9+eTWffF3S9LHwvfV7eHvNN9xx5lAGZyaEOEJjzGHBvb3SWvQ9xGf5pby8vICXlxcwsncih2oaKDxQzfBeCdw2fXCowzPGNGMtetMlPzl/JKpQeKCaKYPSeO3704iOsIuuxoSTYN91Yy36HubKidncdsZg1hQcZFBmPAnR9iM2JtwcbtFb141ph5p6F2+v+YbnFnsmgHc6BBFhvF1wNSZsNd1eaS16cyxbSyp4bvEuXl1RwMHqegZnxvPrS8eQFh8V6tCMMW1o6roJ0vks0XcjdQ1u3t+wl2cX7+TLbfuIdArnje7Nt6cM5OTBafakqzHdhAY501ui7wYKyqqYv2QXLy4toLSiln4psfz4vBFcldufzEQbkdKY7ubwoGbWR2+Apz7bzq/f2YAAM0Zm8e0pAzl9eCZOh7Xejem2wnlQMxN8e8trUIUP/vMMhthDT8b0CMHuo7f76MPcxIGpAOyr8DWBlzGmOzo8THGYTDxiQqsx0S/buT/EkRhj/KWpj96GKTbgGU9+SGY8y3aUhToUY4yf2BAI5ii5A9NYtmM/brfNmW5MT+DWMJsz1oRebk4q5TUN5JdUhDoUY4wfNLbog3X3nCX6bmBSThoAS3dYP70xPUFjiz5Yd0lbou8GBqbHkZEQxdLtluiN6Qlc7sZEH2YtehFxishKEXnbuz5BRBaLyCoRWSYik1upt0NE1jaW81fgxxMR4dShGXy4qZjK2oZQh2OM6aLGy21hl+iBu4CNzdYfAf5bVScAD3jXW3Omqk5Q1dyOh2gArp86kEM1Dby2sjDUoRhjuqhx9EpHkPpU2nUaEckGLgSebLZZgcbZpZOBIv+GZpo7aUAqY/sl8/QXO5p+SYwx3ZNLw7PrZi5wL+Butu2HwO9EZDfwKHBfK3UVeE9ElovInNZOICJzvF1Ay0pKStoZ1vFDRLjplBzyiyv4PH9fqMMxxnTB4a6b4JyvzUQvIrOAYlVd3mLX7cCPVLU/8CPg760cYpqqngRcANwhIqf7KqSq81Q1V1VzMzMz2/8KjiOzxvchIyGKf36xPdShGGO6QMOwRT8NuFhEdgAvADNE5FngRuA1b5mXAZ8XY1W1yPu1GHi9tXKmbdERTq6dPIAPNxXz9hrrKTOmu3KHW6JX1ftUNVtVc4DZwEeqeh2ePvkzvMVmAFta1hWReBFJbFwGzgXW+Sn249KtZwxh0sA07nphFe+u+ybU4RhjOsHl7QQPm0R/DLcAvxeR1cDDwBwAEekrIgu8ZXoBn3nLLAHeUdV3uxLw8S4+OoI/XD0etyqvLC8IdTjGmE5wB/mumw6NR6+qeUCed/kzYKKPMkXATO/yNmB8V4M0R/rD+1/jFOHuc0aEOhRjTCeEYx+9CSN5m4t5bUUht08fwqi+SW1XMMaEne7UdWOCrKK2gftfX8fQrATunDHUZxm3Wyk6UG332hsTxsK668aE1u/e3UTRwWpeue0UoiOcTdvrGtx8sbWU9zbs5eVlu6l3KS/fNrVpMDRjTHgJdteNJfpuYumO/TyzeCc3nZLDxIGpVNY2kLe5hIXr97BoUzGHahuIi3LSNyWWnfuqKKu0qQeNCVfBHtTMEn03UFPv4ievriEhOoLBGfF87+llfLKlhLoGN+nxUcwc24dzR/di2tAM8osrmPWnz0IdsjHmGBqfjHVaojeN/uuNdWwrqfQsv7mePskxXDt5ABeM6U1uTlrQJi8wxvhH0wxT1kdvGpVVebphbj1jMBeM6cP47OSgTUFmjPE/DfIwxZbou4Enb5wU6hCMMX7kshmmTFdYQ9+Y8Bd2Y92Y7snuojcmfAW768YSfTdWXeeyB6OM6YYO314ZnPNZou9m3G7ltRUFXDNvMSc88C7PfrUr1CEZYzqosesmWHfM2cXYEKisbcAhQmyUs+3Czagq97+xlvlLdjdt21dRe0QZwTrpjQl3jffRB+vuOWvRB9kf3tvM6F8sZNJDH/DF1tJ211NVHnxnI/OX7Ob26UN47JoTAUiMiQxUqMaYAHG7NWjdNmAt+qBbU3gQ8AxQdv3fl7D14ZkAvLtuD4+8u4krc7M5dWgGSTGRuFVZumM//VPjeGVFAa+tKOSmU3K497wR7Kuso19KLE/kbWVIZjzTR2SF8mUZYzrArRq0C7FgiT7o/nj1iYz/1XuA54LMos3FVNW6uOfl1VTXu3jk3c08wuaj6jkE7jprGD88exgiQkZCNE/fPIk5zyznu08vY+EPT2NoViIVtQ0APPXZdipqGrhiYnZQX58xpm1uDd4dN2CJPujq3e4j1r/zj6UAJMdG8sYd01ixq4zYSCfFh2qIdDoYnJlApFPIToljQHrcEXWHZiXy8m1Tmfr/PuK3724mMSaC11YUAvDV9v18tX0//VJjOXlwenBenDGmXRQN6jMvluiD7Kq/fgnAy7dNpb7BTVlVPb2SohmWlUhyXCQjeid26HjpCdHUudy8v2EvAN+ZlsPYfsnc/dJqABvF0pgwpNai77lUlYKyasb0SyJ3YGpArrj/bOYJRDodTYm+vKbe7+cwxnSN2x3cFn2777oREaeIrBSRt73rE0RksYisEpFlIjK5lXrni8hmEckXkZ/6K/DuaNHmYupcbm6YmuPXJD9rXJ+m5UpvH/0frhrP1bn9rdvGmDCkBLdF35HbK+8CNjZbfwT4b1WdADzgXT+CiDiBvwAXAKOAa0RkVKej7eaeW+x5uOni8X39etzfXDGuaXnV7gMAXH5SNr+9chwD0+P9ei5jTNe5NQz76EUkG7gQeAi427tZgcbZqZOBIh9VJwP5qrrNe5wXgEuADV2IudvKL6kAINLpeX8te/ElInplEZGS0qXjOoHXpyfx3oa9DCvdQXW1rx9FxzmSkogePNgvxzLGHKZKUB9tbG8f/VzgXqD5lcIfAgtF5FE8nwxO8VGvH7C72XoBMKXDUfYQ1XUunA7B6RDU5WLPL37ht2PHABcD5fOg3G9HhSHvLSRqwAA/HtEYA8F7KhbakehFZBZQrKrLRWR6s123Az9S1VdF5Crg78DZLav7OKTPUbhEZA4wB2BAD00skU4Hw7ISaHC5qf74YwBSvvUtEs89J8SRHa165SpKH38cd0VFqEMxxnRRe1r004CLRWQmnoZjkog8C1yEp98e4GXgSR91C4D+zdaz8d3Fg6rOA+YB5Obm9sghGf/z3OHc/dJqzvmfT3hizb+I6NWL3r94AIkIv5uftM5uyzQmkII58mybF2NV9T5VzVbVHGA28JGqXocnYZ/hLTYD2OKj+lJgmIgMEpEob/23/BJ5N3T5SdlcO2UANTt2oksWE3np5WGZ5I0xPUtXBjW7Bfi9iKwGHsbb7SIifUVkAYCqNgB3Agvx3LHzkqqu71rI3dvDl41lbtw2GsTBZTsz2VpiXSPGHI+C2W3RoeakquYBed7lz4CJPsoUATObrS8AFnQlyJ7EXV1NyscLqT1tOqSl84PnV/LGHdOIirCBRI05XgR7ys8e02/gVjdz3ptD34S+jMkYg1vduNWNok3LjesOHIgIDnHgEAeqiqJNX31pOc67ryvm7SmT9uEqcg4eZPu5/Tg3aRsv5mXwyvICrp3SMy9AG2NCr8ckekH4as9XALye/3qIo2ndw680sCsDfl7xPFQKCcOd/PeXJ/HPr+PxvArPm4M0LoscXj5i/XCpBrfilGjuP/3bnDxgeOhenDGm/YLYd9NzEr0IVwy7gk8KPqGkuoQHpz3I6dmn4xBv6x1P672RW924ceN2u5ta3g5x+JyhqWUr31ervz1X0GvXrmffnu+SdN89LLrqEm557xb2VOyjMmkze+tBRfH89BuPpc3O1mKfHC7jGQnPxS2LXmNYzPm8dvXv2ozFGBM6wZ4JrsckeoBfnvJLKuoqmDp/Kk+sfoKZg2cS6QifGZiKXn0bR1wcva+4GmdsAq9f4r9PHiuLtvO9BT9kC++y48Dd5KT0abuSMSZkgnkxtsddAUyISuCCnAsorCjk0jcuZWf5zlCHBEBDWRnlCxaQfOklOBMS/H78E/sOYs7YOwC4+a3/8vvxjTH+E+yLsT0u0QP8+tRf8+gZj1JYUcirW14NdTgAHHztdbS2lpTZswN2jpkjPTdBlehXvLP5K8b+80R+uOCxgJ3PGNN5YfXAVHcU7YxGVXGpC6c4Qx0O6nZT9sILxOXmEjM8cBdLH/74uablny7+HkgDH5b8jQue+w9q6m1cemPCRZAb9D0z0QOMTh9NpCOSpXuWUu8ObZKr/Pxz6nfvJvXaawJ6nh9NvfqobbGukRQ0LGL6v75NXUNDQM9vjAlPPTbR90/qz8OnPszqktU8tiK03Rdlzz2PMyODxLNbjvnmX8Mze7H2xrWsun4Vd458lMdOfYGvvvMSE5NmU+ncyIKvlwX0/MaY9gvbJ2O7m/MHnc+yvcv45/p/clLWSZw54Mygx1BXUEjFxx+TcfttSFRUUM7pdDi5dcp5Teu947OgHJKi445RyxgTLHYx1s/unXQvJ6SdwP2f309RhX8m5OiIAy++CA4HKVddFfRzA7jdbj4oeBNHQwbTB40JSQzGmKMF8Vpsz0/0UW43v5/2MBV1FbyZ/2ZQz+2uq+PAK6+QOONMInv3Duq5Gz298kNqnTs5p9/VOBw9/sdtTLcQzElHoId33bAtD565hP6OCPqPmcKWA75GUg6cQwsX4iorI/WawF6EPZYn1z4FAv81/YaQxWCMOVpr42oFQs9u4lWWer7GpjI0ZShbyoKb6Muee56oQYOImzo1qOdtVO+qp1zWAbBmz9aQxGCMOZrdXulPY6+EH2+FuzcyLHU4uw7totZVG5RT12zYQPWqVaReMzvoH9MANhUXcs7zNwLQx3E6U7JHBj0GY0x46NmJHiA+A5yRDE0dilvdbD+4PSinLZv/AhITQ/KllwblfM39Y8VCrnznIkpd65madBsLr/szURHhM+aPMSa4F2N7dh99M8NShgGwpWwLI9MC27p1lZdz8O23Sb5oFs6kJJ9l3G43N7z+a0qq9x6xfU/tRqIkBfD04Yl3qYkogjTbJwiOpv8gHNKdON3p/PnsuZyWM9rvr88Y00U28UhgDEgaQKQjMigXZA++8SZaXU3qtde2WqagfB+rK15BXTE4NLZpu0oDNewhwZHduKFZLTmc8pstKG48U6o0AG4SGMB9U39iSd6YMGYPTAVApCOSQcmDyC/LD+h5VJWy+fOJnTCBmBNOaLP8mb2u508X/kdAYzLGhBdBgprpe34ffTNDU4aSfyCwib5q8WLqtm8P+Lg2xpjuyyHBvb2y3S16EXECy4BCVZ0lIi8CI7y7U4ADqjrBR70dwCHABTSoam4XY+60YanDWLB9AT/79GdI45R83q+Ns08dte4t0y+hHzeOvrHNc5Q9Px9naiqJ553XZlljzPFJBNxhejH2LmAjkASgqk1DJYrI74GDx6h7pqqWdipCPzq136m8mf8my/cu907A55k4HKVpveVE4YpysNbz0hbtXkR8ZDy79lWRX1wJQEpcFFFOz5tC6qE6HvxgNe+f3Ic3X7zVx3Rhh9drXBXH2ecpY0wjh0hQx6NvV6IXkWzgQuAh4O4W+wS4Cpjh9+j8bGTaSP592b87XG9NyRp+u+S3VNVXUVVfxTcVFUikC1AO1gPeUZDPXlGOUyG7cD/Xve55czj8ozz6hypEMTp9OYWLftyZlxNQ9Xu+CXUIxvRYQni26OcC9wKJPvadBuxV1dZuZ1HgPRFR4H9VdZ6vQiIyB5gDMGDAgHaGFRzjMsfx3IWeST0qahu48okv2LTnEACRTqHe5fmJXX5yKZGFz3MiQFU7D161h+rde/wftB/EjBlDZL9+oQ7DmB6n8SFKVQ3KA5VtJnoRmQUUq+pyEZnuo8g1wPxjHGKaqhaJSBbwvohsUtVPWhbyvgHMA8jNzQ3mnUcd4lZtSvJAU5IHWDL6dL4zp+1+fGPM8a0xt6sGZ8ji9rTopwEXi8hMIAZIEpFnVfU6EYkALgcmtlZZVYu8X4tF5HVgMnBUou8ukmIiWfvLc/nk61LyiyuoaXDxRN5WRvVJ4qrc/qEOzxjTDTi82d2tiiMIT0+1eTlQVe9T1WxVzQFmAx+p6nXe3WcDm1S1wFddEYkXkcTGZeBcYJ1fIg+hxJhILhzXh1OHpfP3T7czbWg6b905jfjo4+axBGNMF3S3Qc1m06LbRkT6isgC72ov4DMRWQ0sAd5R1Xe7eM6wUHSgmlv/tYI+KTH85dqTiHDaLTTGmPDUoSaoquYBec3Wb/JRpgiY6V3eBozvSoDhqLrOxZx/LaOm3sX8W6aQEhecKQKNMaYzrK+hg9xu5Z5XVrO+qJy/35jLsF6+bkQyxpjwYf0NHfTIws28s+YbfnL+SGaM7BXqcIwxpk2W6DvgmS938NePt3L9yQO59fTBoQ7HGGPaxRJ9O+VtLuaXb63n7BN68cuLR4dk1ihjjOkMS/TtsL20kh/MX8nwXon8cfYEnA5L8saY7sMuxrahoraBMx/NA+DBS8fYvfLGmG7HWvRt+Mmra5qWX1y6O4SRGGNM51iib8MJvRM5f3RvAF5eXsChmvoQR2SMMR1jib4Nd84Yxl+vn8gI7/3yn24J+bD6xhjTIZbo2ykxxtM3v2xHWYgjMcaYjrFE306j+yYBcPUkG6HSGNO9WKJvp/5pcQCkxEWGOBJjjOkYS/TtNCQrAYC/frw1xJEYY0zHWKJvp9hIJwCffF0S4kiMMaZjLNG3Q3Wdi9nzFgNw86mDQhyNMcZ0jCX6dnhrdWHT8uxJ4TVxuTHGtMUSfTu8vMwzU+Lvrhxn49wYY7odS/RtqKl3sWyn5975C8f1CXE0xhjTcZbo21B0oLppedOeQyGMxBhjOscSfRsGZyY0Lf/hva9DGIkxxnROuxO9iDhFZKWIvO1df1FEVnn/7xCRVa3UO19ENotIvoj81E9xB1RVXQNVdQ3Uu9xU1jYAkJ0ay7wbJoY4MmOM6biODK5+F7ARSAJQ1asbd4jI74GDLSuIiBP4C3AOUAAsFZG3VHVDV4IOpMXb9nHN3xajeuT2KydmExdlY9EbY7qfdmUuEckGLgQeAu5usU+Aq4AZPqpOBvJVdZu37AvAJUDYJvr84gpU4T9mDCXC6SAqwkFiTAQXjLELscaY7qm9TdS5wL1Aoo99pwF7VXWLj339gOazdRQAU3ydQETmAHMABgwI3b3qB6s9481//8yhxHifhjXGmO6szT56EZkFFKvq8laKXAPMb626j23qYxuqOk9Vc1U1NzMzs62wAuZgdT3REQ5L8saYHqM9LfppwMUiMhOIAZJE5FlVvU5EIoDLgdauUhYAzcf1zQaKuhJwoJVV1tkIlcaYHqXNFr2q3qeq2aqaA8wGPlLV67y7zwY2qWpBK9WXAsNEZJCIRHnrv+WHuANmyY797C2vZV9FbahDMcb0cD67NwKgq/fRz6ZFt42I9BWRBQCq2gDcCSzEc8fOS6q6vovnDKgZI7MAmPjgB023VhpjjD9JkEdS6VCiV9U8VZ3VbP0mVf1rizJFqjqz2foCVR2uqkNU9aGuhxxYv7hoNFMHpwPw4tLdbZQ2xpjwZ0/G+jB/zskMzUrg/Q17Qx2KMcZ0mSX6Vpw3uhdLduznQFVdqEMxxpgusUTfihkjs3C5lcXb9oc6FGOM6RJL9K0Y1ScZh8DGb8pDHYoxxnSJDd7SQnWdi6U79uNyK26F9UVHDeFjjDHdiiX6Fv75xQ5+++6mpvX9ldZHb4zp3izRtxDhnSrwX9+dTGJMJDnpcSGOyBhjusYSfQuxUZ4xbkb0SiQrKSbE0RhjTNfZxdgW+qZ4kvu20soQR2KMMf5hib6FkwakArDcOyG4McZ0d5boW0iJi2JYVgLLdtj988aYnsESvQ+5Oaks31mG2x2sseWMMSZwLNH7MCwrkfKaBvYeqgl1KMYY02WW6H0or/FMJ5geHx3iSIwxpuss0fuQHh8FwIFqe1jKGNP9WaL3ISPB05IvPWSJ3hjT/Vmi9yEj0ZvobTpBY0wPYInehzRv102ZjUVvjOkBLNH7kBbnSfT7KizRG2O6v3YnehFxishKEXm72bYfiMhmEVkvIo+0Um+HiKwVkVUisswfQQdafLRnCKCqOpsc3BjT/XVkULO7gI1AEoCInAlcAoxT1VoRyTpG3TNVtbTzYQZXVISDCIdQVecKdSjGGNNl7WrRi0g2cCHwZLPNtwO/UdVaAFUt9n94oRMb5bREb4zpEdrbdTMXuBdwN9s2HDhNRL4SkY9FZFIrdRV4T0SWi8ic1k4gInNEZJmILCspKWlnWIETF+Wkpt4SvTGm+2sz0YvILKBYVZe32BUBpAInAz8GXhIR8XGIaap6EnABcIeInO7rPKo6T1VzVTU3MzOzQy8iEGIjrUVvjOkZ2tNHPw24WERmAjFAkog8CxQAr6mqAktExA1kAEc0x1W1yPu1WEReByYDn/jxNQREbFSEJXpjTI/QZoteVe9T1WxVzQFmAx+p6nXAG8AMABEZDkQBR1xwFZF4EUlsXAbOBdb58wUESlyUk+p6u+vGGNP9deU++qeAwSKyDngBuFFVVUT6isgCb5lewGcishpYAryjqu92LeTgcIrgsmGKjTEBEBXhSb3BelanQ3PGqmoekOddrgOu81GmCJjpXd4GjO9qkKFQ63KTHBUZ6jCMMT1QTKRnbup6l7uNkv5hT8a2oq7BTZTTvj3GGP/7YGMx/VJiyU6NDcr5LJO1oqqugehI+/YYY/xr174qPttSwmUn9sP3jYr+Z5nMh+o6F7v3VzEkMyHUoRhjepjVBQdwK1TVuagO0p19luh92Lz3EG6FUX2SQh2KMaaHmTY0gxG9Ennq8+385v82BuWcluh9WF90EIDRfS3RG2P8Ky0+ioU/Op3x/VP4fOs+DlbVB/ycluh9+HLrPjISopsulKgqlz3+OefP/YS95TZhuDGm626elsPWkgrOnftxwEfKtUTvw1fb93PasIymCyXlNQ2s3HWATXsOcfuzy9m859Ax61fXudhaUoHnoWHf6l1uPvm6hPc37LXhkI05Dl0yoR83TxvE3vJaDgS4Vd+h++iPB263UlpRy+srC/l0Sym19S4qvYlYBNYVlXPZ45/z5I25nDIko6mey62sLTzI8p1l/O/HWyk+VMvEgalcdmI/NnxTTumhWlLiIomNdLKnvIYVuw5QcsgzVeHI3on88zuT6Z0cE5LXbIwJjdyBqfz9s+08npfPg5eODdh55FitzlDJzc3VZctCN0fJk59uY31ROTGRTmIiHcREOomPcnJVbn/cCjc89RU79lXxnWk59EuJZWtxBQvW7WlK3BMHpnLWCVk8t3gXhQeqSYyOoE9KDOXVDVTWNdArKYYRvRK5ZEJfXG7lnpdXM7x3Ii/dOpVIu3ffmOPKA2+u49nFO1ly/9lkJER3+jgislxVc33tsxa9D987bfAx9784Zyrff24Ff/tkG26F6AgHM0ZmccHYPozrl8zA9DhEhFtOG8yO0koGZcQTcYwE7lLlzudXMn/JLm6YmuPnV2OMCWfnje7NM1/uZENROacPD8zIvZboOyE1Por5c07G5Vb2V9YRF+Vsmn6wuUing2G9Ets83nmjewME5eq7MSa8bPymHIABaXEBO4cl+i5wOoTMxM5/1GpUVet5aCLOx5tFcxW1DTz12XY2FJWTlRTNGcMzOeuEXl0+vzEmdJwOz00f723Yw5zThwTkHNYhHAbE4flhf7Bhr88n5epdbp75cgfTf7eIP7z/NZv3HuK1FYV89+ll3PXCSg5UBWcEPGOM/90wNYcLxvTm4QWb+HRLYGbXsxZ9GEiKieS3V4zj3ldWc83fFjPn9MGkxkURE+lgb3kNjyzczLaSSqYMSuPJG09gQv8U6l1unsjbymMfbuHLrfv4w1UTOHVYRtsnM8aEFadD+M9zh/N/6/bw+opCThvm/356S/Rh4sqJ2cRGOrn/jbV8/7kVR+zLSY/j7zfmMmNkVtO9/ZFOB/9x1jBmjMzihy+u4vqnvuKyCf146LKxxEY5Q/ESjDGd9NX2/YAnDwSCJfowcuG4Ppw6LIP84gpq613UNrhBYOrg9Kbxq1sa0y+Zt+6cxh8/3MK8T7axu6yKJ2+cRHKsjaVvTHfxzBc76ZUUzcSc1IAc3xJ9mEmOjWTiwI79sOOiIrjvghMY0zeZu19axflzP+He80dwyfh+OBzBGQbVGNM5Ly/bzea9h/j5hScQHRGYT+N2MbYHuWh8X16YczIZCdH86MXVXPbEF+yvtAu1xoSr7aWV/PiVNQDMGtc3YOexRN/DTByYxpt3TOP33xrP6t0HePS9zaEOyRjTioyEKNLiowDP7ZWBYom+B3I4hCsmZjN1cDrPf7WLhiDNS2mM6ZjEmEg+/vF0ThqQwmMf5h9zIMSuaHeiFxGniKwUkbebbfuBiGwWkfUi8kgr9c73lskXkZ/6I2jTPo1X8NcWHgxxJMaY1iTGRHL5SdmUVtSyeNv+gJyjIy36u4Cm6VBE5EzgEmCcqo4GHm1ZQUScwF+AC4BRwDUiMqpLEZt2mz4ik4yEaO59ZQ0ud/gNXmeM8TjrhCwA8r4uDsjx23XXjYhkAxcCDwF3ezffDvxGVWsBVNVXhJOBfFXd5j3OC3jeHDZ0MW7TDukJ0fziolH8YP5KFm0q5uxRNlyC8b83VxXy8IKN1DW4catnqG+3qmdZFVVQDjc0hGZ3gskRXzzLcnQ58Vmu2f6jFg4vHlHuGMcR77IIOAQcIi22CQ45vC5AhMNBTJSTCIdQXl2P0qyut3zLes3XHeJ5JqZx1NoPNuzlvgtO8P2N7oL23l45F7gXaD5C13DgNBF5CKgB7lHVpS3q9QN2N1svAKb4OoGIzAHmAAwYMKCdYZm2nD+mN32SY/hLXj7TR2QecxRNYzpq+c79/OTVNQzJTGDiwFRvEvMmOMfhxNaYWJt/rmzsjm7+JoD6KqdHlG/PcXx1dTcdp5W6njckTzn1vkm51VNG1XP0w9s8Zepdbmoa3LjcbrISE3CIeMscLufWo+s1lXFDZUMDdd7raFMGp7fyne6aNhO9iMwCilV1uYhMb1E3FTgZmAS8JCKD9cirCb5u4vbZh6Cq84B54BmPvl3RmzZFOh38+LwR3P3SauZ+sIV7zhsR6pBMD7Bl7yEeWbiZDzbuZUBaHE/dNIleSTZxTrhqT4t+GnCxiMwEYoAkEXkWT+v8NW9iXyIibiADaD4qTwHQv9l6NlDkl8hNu11+UjbvrPmG11cWWqI3x1TvcrO9tJL84grKq+sZl53CiN6JTSMs7jlYw/wlu3gibyuxUU5+cOZQvnvaYHsSO8y1mehV9T7gPgBvi/4eVb1ORG4DZgB5IjIciAJKW1RfCgwTkUFAITAbuNZv0Zt2m9A/hQ83FVNZ2+Bz7HzTc+0tr+HVFQUcrK7nplNy6JPsmfT+UE09BWXVFB+qZW95DV9u3ccHG/ZyqPbIOYwbZ0jbV1HHPu8DeBeM6c2vLhnjl2G6TeB15S/+KeApEVkH1AE3qqqKSF/gSVWdqaoNInInsBBwAk+p6vquh206an9VHdERDmJbGTPH9EzrCg/y7Se/4mB1PU6H8I/Pd3jHTnKwaFNJU98wQEpcJOeP6c20oRkMzUogPjqCVbvLWLnrAHsO1jBxYBoD0uI4f0xvBmXEh/BVmY7qUKJX1Twgz7tcB1zno0wRMLPZ+gJgQVeCNF03MC2O2gY320orGZqVEOpwTBAcqKrju08vJSE6gldum0pslJO/fryVlbsOUFZZx7VTBjApJ42spGgyE6Lplxp71JzFgzLiuezEwIyoaILHPsMfJ2aO68PDCzbxzy+2B3S2eRM+fv7GOvZV1PHGHdOaprS0n/3xyRL9cSIrMYbzxvRm4fq99sfewxQfquFAVT37KurYvb+KXfuryC+u4N31e7jn3OGM6Zcc6hBNiFmiP4643G6iI+w++u7uYFU9S3bs5/P8Uj7PL2VLccUR+50OoU9yDFflZnPbGYGZg9R0L5bojxOqymdbSjl3dO9Qh2I6qMHl5uXlBbywdDc791VyoKoegJhIB5MHpXPlxGz6psSSGhfFgLQ4+qTEHNXXbo5vluiPIxW1DcREWgLoLlSVhev38sjCTWwrqWRMvyQuHNuH/mlxjMtOZuLA1IBNVGF6Fkv0xwkR4dIT+/Hi0t3cdMogu/MmTLncyqY95SzZvp/XVhSytvAgQ7MSmHf9RM4Z1euIcVuMaS9L9MeRn808gbfXfMNzX+3kFxeNDnU4phm3W3n2q5089uEWSis8DyUNyYznkSvGcflJ/WyMItMlluiPIxkJ0UwckMrKXQdCHUqPsXt/FV9u20dNvYvRfZM5sX9Kp+bp/dNH+fzPB19z8uA07r+wP5Ny0shOjQtAxOZ4ZIn+ONMrKZplO8tCHUan1Ta4WF9UTv/UuJA/fv/mqkJ+9tpaKutcTdtG9Uni+VumkBIX1WZ9VWVfZR2fbSll7odfc/mJ/fj9VeOte8b4nSX640xBWXVYjjJYXlNPYnTEUUmupt7Fgap6PtlSwocb9/LpllKqvIn1lCHpzL16AlnNXk/jWOiB7OrYvb+Kx/O2Mn/JLnIHpvLgZWNIiY0ib3MxD7y1nhv/sZRnbp581EBfh2rqWb6zjE++LuWLraXs3FdFdb3ntYzqk8RDl421JG8CwhL9ccTtVtYVHeSayeEx3n9NvYt/fbmTN1YVsr6onPT4KMb3T2Fsv2RcbmXh+j1H3CPeJzmGy07sx7ShGWwvreTPH+Vz1h8+5pxRvRjdN5l/ry5iTcEBIpwOJuWk8suLRjc9EdpVbrfyaX4p//pyBx9uKkaAm6cN4mczRza9qcyePID0hGi+/9xyrvrrl5w3uhf7q+ooPVTH1pIK8ksqUIXoCAeTB6UxbWgG2amxZKfGMXVIOrFRdgeNCQwJ1GS0XZGbm6vLli0LdRg9TnF5DZMf/pBfXTKaG6bmhDSWitoGbnl6GV9u28f47GTOHJlFQVk1q3cfaEqIk3JSOX1YJvHREUwZnMaoPklHtHi37D3EE3lb+WDjXsprGshJj2Pm2D7UNrh5Y2Uhh2oamDY0nfNG9+b8Mb3b1Z3S0sGqel5evptnF+9kx74qMhKimD1pANdOGUDflFifdRZtKuZXb29ge2klKXGRZCZE0z8tjvHZKZw4IIXJg9KIscHljJ+JyHJVzfW1z1r0x5HEmEiinA4KyqpDHQoPvbORL7ftY+7VE7j0xH5H7KupdyFCm/eID+uVyB+unkCDy83+qjrS46Obxk2/9fTBzPtkG++u38OizWv5S14+7//oDJ8JtsHlprbBjdMhrNhVxui+ySTHRvJ4Xj5/+jCf6noXEwem8qNzhnP+mN5txnXmyCymj8ikwa324JIJC5bojyOxUU6mDknnn1/sIDrCwU2n5JCeENwLmos2F/Pyst0sWLuH6SMyj0ryQIdbuxFOB1mJR153yEqK4eezRnH/hSfwwcZibnlmGc99tYvvnjoIgC+37uNnr6+lsKyaBrdnrtPYSCfV9S56JUUzZVA6b60u4rzRvfjBjGEdHi9GRIh0Wn+7CQ/WdXOc2VdRywNvrmfBum9Ij4/m0W+N44zhmQG/CKiqvL3mG/7z5dW43MoNUwfyw7OGkxwXnJmJpv9uEaP6JvH4tyeyrvAgV//vl2QlxXDe6N5EOgWnQ9hXUcfY7GSe/HQbX++t4JrJ/Xno0rGdul3SmGA7VteNJfrj1Iaicu58fgXbSisZ3TeJSTlp9E6OYdqQDEb3TfJbcssvPsTjeVvJ21zC/so6xvZL5qmbJgX91shL//I5O/dVMnvyAJ7I2wrA4vvOonfy0XcgqSq1DW7rRzfdiiV641NNvYs3Vhbyr8U72b2/ivIazxRymYnRXDi2DxdP6MuJ/VM61NpXVQ5U1eNwCP/4fDt/WZRPdISTc0f1YuqQdC4/KbupHz2YXl9ZwL2vrKHepfROiuH/XTGWM0dkBT0OYwLFEr1pl+JDNXyRv4//W/cNizaXUNfgZmTvRB6YNYrU+CjeXlNETb2bmWP7MHFg6hF1G1xu/ueDr3l1eSF7ymuatl88vi8PXDSKjCBfC/Clpt5FhENsOAHTI1miNx1WXlPPu2v3MPeDryk66EncDvGMde5yKy/dOpXcnDTcbuW9DXt5PC+fNQUHOWdUL6YMSqPBrYzrl8wpQzNC/EqMOT74JdGLiBNYBhSq6iwR+SVwC1DiLfIz7/ywLevtAA4BLqChtUCas0QfPqrqGnh79Tc4HMLpwzOIjnAy60+fUlBWTVZiNC63UlpRR/+0WO45dwSXTDj6LhpjTOD56z76u4CNQFKzbf+jqo+2o+6ZqlragXOZMBEXFcFVk/ofse35753MGysL2V1WRYNLOXNkFheM6W1dIsaEqXYlehHJBi4EHgLuDmhEJuz1T4vjB2cNC3UYxph2am8TbC5wL+Busf1OEVkjIk+JSOrR1QBQ4D0RWS4ic1o7gYjMEZFlIrKspKSktWLGGGM6qM1ELyKzgGJVXd5i1xPAEGAC8A3w+1YOMU1VTwIuAO4QkdN9FVLVeaqaq6q5mZmZ7Y3fGGNMG9rTop8GXOy9qPoCMENEnlXVvarqUlU38Ddgsq/Kqlrk/VoMvN5aOWOMMYHRZqJX1ftUNVtVc4DZwEeqep2I9GlW7DJgXcu6IhIvIomNy8C5vsoZY4wJnK4MavaIiEzA0we/A7gVQET6Ak+q6kygF/C698nKCOB5VX23KwEbY4zpmA4lelXNA/K8y9e3UqYImOld3gaM71KExhhjusRufDbGmB7OEr0xxvRwYTnWjYgcAjaHOo5WZADh/JRvOMcXzrFBeMcXzrFBeMcXzrGB/+IbqKo+700P1xmmNrdnTJxQEJFl4RobhHd84RwbhHd84RwbhHd84RwbBCc+67oxxpgezhK9Mcb0cOGa6OeFOoBjCOfYILzjC+fYILzjC+fYILzjC+fYIAjxheXFWGOMMf4Tri16Y4wxfmKJ3hhjeriQJnoR+ZaIrBcRt4jkNtt+jnf8+rXerzOa7csTkc0issr7Pyuc4mtW5i0RCdgAbp383r0rIqu99f7qnR4yLOITkTgReUdENnnr/SZcYvPue0hEdotIRaDi6mJ8E73b80XkMfEOMBXE2NJFZJGIVIjIn1vUudo7b8V6EXkkEHF1Mb5rvN+7Nd6/kYBMdNzR2EQksVmeWyUipSIyt1MnV9WQ/QdOAEbgGT8nt9n2E4G+3uUxeOapbdx3RNlwi8+77XLgeWBdOMUGJHm/CvAqMDtc4gPi8Ew5CRAFfApcEA6xeddPBvoAFeH4ewcsAaZ6f7b/F4LvXTxwKnAb8Odm29OBXUCmd/1p4KwQfO9aiy8CKAYyvOuPAL8Mh9h81F8OnN6Zc4f0gSlV3QjQsvGhqiubra4HYkQkWlVrgxhep+ITkQQ80y3OAV4Kp9hUtdy7PQJPMg3YlfhOxFcFLPKWqRORFUB2mMRWq6qLfdUJh/iANDxv4l966z0DXIon4QcrtkrgMxEZ2qLKYOBrVW2cNu4D4ArgQ3/H1sn4xPs/XkT24ZkTOz9MYjscpMgwIAtPA6jDukMf/RXAyhZJ/h/ejzL/FaiPqB3QMr5f45ltqyp0ITU56nsnIgvxtGAOAa+EKjAvXz9bRCQFuIgAJYN28hlbGGkeXz+goNm+Au+2cJAPjBSRHBGJwPMG1P/YVYJHVeuB24G1QBEwCvh7SIPy7RrgRfU27Tsq4C16EfkA6O1j1/2q+mYbdUcDv8UzYUmjb6tqoXgmNHkVuB54JhziE8/4/ENV9UciktPZmAIRWyNVPU9EYoDngBnA++EUnzcZzAceU88w12ETmz/5OT5fjZ1Of1rrSmxHBaFaJiK3Ay/imXP6Czyt/E7zZ3wiEokn0Z8IbAP+BNwHPBjq2FqYjSfXdUrAE72qnt2ZeiKSjWfqwRtUdWuz4xV6vx4SkefxTE3Y6UTv5/imAhPFM+1iBJAlInmqOj0MYmt+3BoReQu4hC4k+gDFNw/YoqpzOxtXAGPzGz/HV8CR3VzZeFqnQY3tGMf7N/BvABGZA7i6eDx/xjfBe8ytACLyEvDTzh7M3987ABEZD0To0fN2t1tYdt14P7q/A9ynqp832x7ReEXc+048ixBMTdhafKr6hKr2Vc+0i6fi6ZucHg6xiUiCeKd/9LaaZwKbghnbseLz7nsQSAZ+GOy4vOdPoZXYwsExfu++AQ6JyMnerswbgK60Hv1KvHfGiUgq8H3gydBGdIRCYJSINI76eA6wMYTx+HINnk+5ndfVK8ld+Y9nrtkCoBbYCyz0bv85UAmsavY/C8/V6eXAGjwXo/4IOMMlvhZ1cwjsXTcd/d71ApY2+979CU8rIVziy8bT3bCx2fbvhUNs3n2PeOu4vV9/GS7fO+++XDyNnq3An/E+9R6s2Lz7dgD7gQpvmVHe7fOBDd7/AbvTqwvx3eb9vVuD55NHerjE5t23DRjZlXPbEAjGGNPDhWXXjTHGGP+xRG+MMT2cJXpjjOnhLNEbY0wPZ4neGGN6OEv0xhjTw1miN8aYHu7/A8gok2bUwp6OAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#OK, emergency triage here.  No one lives in tract 1442 east of Elma and Aberdeen.\n",
    "# but we will put a few people here to fill the gap.  No voters\n",
    "pt1 = Point(-123.5, 48)\n",
    "pt2 = Point(-123.8,47.5)\n",
    "pt3 = Point(-122.5, 47.5)\n",
    "pt4 = Point(-122.5,48)\n",
    "healPoly = Polygon([pt1,pt2,pt3,pt4])\n",
    "plotPoly(tractMAP)\n",
    "plotPoly(tractGeom[1442])\n",
    "plotPoly(healPoly)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "0c7e8127-d365-4047-a1d7-6fbff2bddcf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tractGeom[1442] = tractGeom[1442].intersection(healPoly)\n",
    "tractPop[1442] = 3.\n",
    "isSkippedTract[1442] = 0\n",
    "notPoly[1442] = 0\n",
    "tractMAP = tractMAP.union(tractGeom[1442])\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "650cf37d-e11a-4ed3-861b-3f3edc411422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.5661375661375662 756\n",
      "100 0.4 420\n",
      "200 0.6052980132450331 755\n",
      "300 0.0 0\n",
      "400 0.4074074074074074 27\n",
      "500 0.2063953488372093 344\n",
      "600 0.358974358974359 741\n",
      "700 0.2827298050139276 718\n",
      "800 0.6927710843373494 664\n",
      "900 0.4573055028462998 527\n",
      "1000 0.398422090729783 1014\n",
      "1100 0.44606650446066504 1233\n",
      "1200 0.5617816091954023 696\n",
      "1300 0.7586206896551724 29\n",
      "1400 0.36834733893557425 714\n",
      "1500 0.23695652173913043 460\n",
      "1600 0.10315186246418338 349\n",
      "1700 0.3141831238779174 557\n",
      "1800 0.5117773019271948 934\n",
      "1900 0.5175675675675676 740\n",
      "2000 0.5599022004889975 409\n",
      "2100 0.5835294117647059 425\n",
      "2200 0.7845303867403315 543\n",
      "2300 0.10126582278481013 316\n",
      "2400 0.2350674373795761 519\n",
      "2500 0.12352941176470589 1020\n",
      "2600 0.242152466367713 223\n",
      "2700 0.0958904109589041 511\n",
      "2800 0.03180212014134275 283\n",
      "2900 0.14732142857142858 672\n",
      "3000 0.14942528735632185 696\n",
      "3100 0.510250569476082 439\n",
      "3200 0.28125 640\n",
      "3300 0.09487179487179487 390\n",
      "3400 0.2828507795100223 449\n",
      "3500 0.3288690476190476 672\n",
      "3600 0.15384615384615385 520\n",
      "3700 0.2661654135338346 665\n",
      "3800 0.4173354735152488 623\n",
      "3900 0.278372591006424 467\n",
      "4000 0.7027027027027027 518\n",
      "4100 0.3644859813084112 214\n",
      "4200 0.6818181818181818 44\n",
      "4300 0.06047516198704104 463\n",
      "4400 0.05042016806722689 357\n",
      "4500 0.07017543859649122 684\n",
      "4600 0.07352941176470588 612\n",
      "4700 0.24786324786324787 468\n",
      "4800 0.0 0\n",
      "4900 0.593320235756385 509\n",
      "5000 0.2902621722846442 534\n",
      "5100 0.7174541947926711 1037\n",
      "5200 0.0 0\n",
      "5300 0.2725450901803607 499\n",
      "5400 0.09360730593607305 438\n",
      "5500 0.3602941176470588 680\n",
      "5600 0.07203389830508475 472\n",
      "5700 0.0 0\n",
      "5800 0.3425184235821852 6242\n",
      "5900 0.18618618618618618 666\n",
      "6000 0.6021505376344086 279\n",
      "6100 0.5734265734265734 143\n",
      "6200 0.5721271393643031 409\n",
      "6300 0.7204610951008645 347\n",
      "6400 0.4659090909090909 880\n",
      "6500 0.5034246575342466 292\n",
      "6600 0.5130890052356021 382\n",
      "6700 0.47368421052631576 285\n",
      "6800 0.5688775510204082 392\n",
      "6900 0.5915841584158416 404\n",
      "7000 0.559652928416486 461\n",
      "7100 0.40676416819012795 1094\n",
      "7200 0.32470892626131953 773\n",
      "7300 0.35995500562429694 889\n",
      "7400 0.6805555555555556 144\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ALL TRIMS COMPLETE. let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    if isSkippedPrecinct[p] == 0 and p%10 == 0: #memory drain if we print all\n",
    "        plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "for p in range (nPrecincts):\n",
    "    if p%100 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a1e3f0d2-c21c-46d4-8227-e75dfd5e89b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and final map areas are 22.011462746299586 20.736234285782647\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAA4I0lEQVR4nO3deXxU5dn/8c81k5WQhC2sSdj3HcLuSi0qolitCxVr26dSbbVWa63+ujxdtLW2PtWqbUVrq1URrVIXUNQibkUgYd9lTwgQwhKSkHXm+v1xJhDihKwzZxKu9+uVV2bOOfc534Rw5eQ+59y3qCrGGGNaL4/bAYwxxoSWFXpjjGnlrNAbY0wrZ4XeGGNaOSv0xhjTykW5HSCYTp06aa9evdyOYYwxLUZWVla+qqYEWxeRhb5Xr15kZma6HcMYY1oMEdlT2zrrujHGmFbOCr0xxrRyVuiNMaaVs0JvjDGtnBV6Y4xp5epd6EXEKyKrReStwPuRIrJMRNaLyJsiklRLu0tEZKuIbBeRe5sruDHGmPppyBn9HcDmau+fBu5V1eHAAuBHNRuIiBd4ArgUGALMEpEhjY9rjDGmoep1H72IpAKXAQ8AdwUWDwQ+Crx+D1gM/KxG0/HAdlXdGdjPS8BMYFPTYp/lSo7BqmehsvzUMqm+QbU3ctqK09Z9nlfMwcJSBFDEWSMg4vnC9id3I852AmhgoVTtUwJbi+fUUaodX8RpWT1S1fbq8aJ4EW8UeKIQjxe8UYgnCo+n+vIoRED9Pvx+H+r3ob5KUB9+nx/VSvAFlqsPAtvg9wGhHJK75ve5OXcdqn1b5tP2LBLILYhI4OdbTv7cVn1Np70PfMjJ9Z7ALgL7wBPY/hRVRfAjnmgGTr6c+ITEkH1NVer7wNQjwD1A9UQbgCuA14FrgLQg7XoA2dXe5wATgh1AROYAcwDS09PrGesslbcZ3vt5k3fTP/BhjHHH8qP7mHDtFzpDml2dhV5EZgB5qpolIhdUW/Ut4E8i8nPgDaA8WPMgy4KeVqnqXGAuQEZGhs2GciY9J8FXn4F/fw/i28G1/4TuozntW3vahDLBl1/w+yVM6NOJB64ciga2UQVV/8nNVPXUOr/zylkW2FXVdlRr4/efWk+gvVZrh6L+UznU70P9fsRfic/nw++vQCsr8fsr8fsqUV8Ffp8PDbxHFY/HC14vHk8UHq/XOfP3evF4vM7nqr8GvF68XucvBI8nNPcehHLyntDtO4T/xfyh2beGMLOqBn7uFU6+JvBaAb+znb/q59kfaAOo39mOwLaBtlWvq/bvfM8F8XipOHGcQW9fg1aUhOxrqq4+Z/RTgCtEZDoQBySJyPOqOhuYBiAiA3C6dmrK4fQz/VQgt2mRDQDDroaO/eClG+DZGXDFYzDi2gbtIq5NW/YVKcUaQ3J8dIiCGmNqKjhyCN4mhF1cp6vzFEdV71PVVFXtBVwPLFHV2SLSGUCcDt2fAn8N0nwl0F9EeotITKD9G82W/mzXbSTMWQo9xsJrN8O7Pw30RddPn5QEPtmezzkPLuE/mw+yO7+Y+15bz8Pvbg1dZmPMyTP9kF4nqaYpg5rNEpHvBV6/BvwdQES6A0+r6nRVrRSR23Au1HqBZ1R1Y5MSm9MldIKvvw7v3Av/fQwOboKv/g3i29fZ9KeXDWFglyReWL6H/3n29EHkfjhtYKgSG3PWO9klF6Yz+gYVelVdCiwNvH4UeDTINrnA9GrvFwGLmhLS1MEbDZc9DF2Hw8K74ampcP086DzojM26t4vnjov6k94xnjvnrw1TWGPMqWsvEdJ1Y1qQsd+Ab7wFZUXw9JdgS92/X+d+tIM756+lXZtTffRJcRE5erUxrYYGum5q3noZKvY/urVJn+j028+/AV6aBRf+BM69G4LccbL1QCG/e2cr04d35U/XjwZg+a4jdGobG+bQxpxdwt11Y2f0rVFyD/jm2zDiOvjgAXjlJucsvxpV5WevbyAxLor7rxxOlNdDlNfDlH6dGNg19A9wGHM2s64b0zyi4+ErT8K0+9Etb5Hzxwv4YFPOyR+wlzOzWbHrCPdcPIgOCTEuhzXmbBPBF2NNCyMCk29nU24BQzf8nuv/+R4JnftyuLic/KIyxvZsz3Xjgj3QbIwJKb+d0ZtmVhbbEYCbJjpFPb+ojPMGpPDCtyfg9YTnB80Yc0oon/INxs7ozwIaGKRs2uDOfPuK4azNKaBvSgJx0V6XkxlzlrOuG9NUlRXlrPvPiySueRoAj8cZSW9UWjt3gxlzlgv3xVgr9K1Q/oFsPn/7cfrueZkxHGE/KXzW707G9x7sdjRjDHYfvWkk9fvZmrWEoo//woiCD5gkPtbFjSV37AMMv/BaukXZP7UxkSKih0AwkaekuJD17/yNDpueY5BvB4Uaz6rOV9H9y7cxYsAot+MZY4KxrhtTX/t2bqTNcxcznkJ2eXqyfMhPGXbpzUxMbOd2NGPMGVjXjam38pIielDIsrSbmfjNh+gdook1jDHN6+QkPTYEgqlL+sCxFGo8nhOHECvyxrQcNgSCqS9vVBS74oeQcnSN21GMMQ1QdTE2XF03VuhbuOLOY+nl20NhwRG3oxhj6i28M0xZoW/h2vabjEeU3Ws/dDuKMaae1G/DFJsG6DXyfPwqFG3/r9tRjDH15PdXAiCe8AxDYoW+hUtM7sBub08S8rLcjmKMqSf1B26vtEJv6utQ+1H0LtmEr7LS7SjGmHqouo8eibBCLyJeEVktIm8F3o8Skc9EZI2IZIrI+Fra7RaR9VXbNVdwc0p0vwtIlBI2fvxvt6MYY+pB/T4AJEzDhDfkjP4OYHO19w8Bv1TVUcDPA+9rc6GqjlLVjIZHNHUZNnUW+bTDv+Ipt6MYY+rB73MKfUSd0YtIKnAZ8HS1xQokBV4nA7nNG62Jsv4Bf78MPvoDFOW5nSakYmLj+Dz1KkacWE7u7q1uxzHG1KGq68YTYX30jwD3cOrmT4AfAL8XkWzgD8B9tbRV4F0RyRKRObUdQETmBLqAMg8dOlTPWLUdUWHxT2DPJ7Dk1/B/Q+CVb8Cuj6s9kda69LnkNvwIexY/7nYUY0wdqi7GEqYn2us8iojMAPJUteZtHbcCd6pqGnAn8LdadjFFVccAlwLfE5Hzgm2kqnNVNUNVM1JSUur/FQTfGVSWwbib4bZMGH8z7FgCz86AJybAZ3+FkmNNO0aE6ZLal/VtJzNo/78pLSl2O44x5gxODWoWIYUemAJcISK7gZeAqSLyPHAT8Fpgm1eAoBdjVTU38DkPWFDbds3K44HEbrDyKSg9Dpf8Fn64FWb+GWLbwjs/hocHwevfg32rQh4nXKInzqE9x1nz9ztPnTEYYyKO3xe4jz5SCr2q3qeqqaraC7geWKKqs3H65M8PbDYV+LxmWxFJEJHEqtfANGBDM2WvLTAs/CEU7HXe+yucz9HxMPoGuHkJzPkQRlwLG16Dpy6EJ8+HVc9Becs+Ex465XKWp3yViXnzKXpwYKvtpjKmxau6j94bWX30wdwMPCwia4HfAHMARKS7iCwKbNMF+CSwzQpgoaq+05TAddr4Gqx8GsZ/B/5fLqRP/OI23UfBFX+CH26B6X9wunneuB0eHgyL7oG8LSGNGCri8ZBxy1wAEsvzYPfHLicyxgTjD3PXTYPGo1fVpcDSwOtPgLFBtskFpgde7wRGNjVkg2x4DZJS4ZIH677QEZfs9N+P+zbsXQaZz0DW32HFk9BzCmR8CwZfAVEx4cneDD5+6zkuAPb2uIz03kEvhxhjXBbui7Gtb+IRfyXEtGnYN1AEek52Pi7+Lax5HjL/Dq/+DySkwOjZMPab0L5n6HI3g7y8gwxZ9Uv2xPQh/Zv/cDuOMaYWWjXWTSTdR9+i9DoH8rfBgUZeCmibAufcCd9fAze8Cqnj4NNH4dGR8MI1sPUdCDzVFmm2PX8nHTlG1JVPIEH+CvH7ldxjJacmJjbGuKJq9MpwTRjU+s7oB10G7/4UtrwFXYc1fj8eD/S/yPk4lu1crF31LMy7DpLTYMxNMOZGSOzafNmbIOvDNzjn+EJWpd3ImKGTTy6v9PlZsfsI72w4wEsrsymv9PPKLZMY16uDi2mNOcupc7IYrgemWl+h79AHBl8OHz4E6ZOgz/l1t6lLuzSY+hM4/x7YughW/g0+uB+W/hYGXgoZ34Q+U8PW31ZTcVEhnZfeQ66nK8NueJAKn59Pt+fzzoYDvLvpIEeKy4mL9tAlKZbsIyUcLS53JacxxmF99M3hyr/Cg+mwcUHzFPoq3mgYMtP5OLzDGWZhzQvOXw/tesLYm2D0jdC2c/Mdsx7WPH8fU3Q/rwx+nBVv7eDdTQcpKKmgbWwUUwd15tJhXTl/YAo7DxUz47FPwprNGPNF4R4CoXUW+ug2EN8O9oVwjPaOfWHar2HqT2Hzm87F2//8Cv7za+g63LlW0HMypE+GhI6hyVBWRPZn/2LC/heY77uAH6/qQGLsAS4a0oXpw7txbv9OxEWH5wfJGFN/J0evjMTbK1uMdfPhxGG48CehP1ZULAz/qvOR/zlseBV2f+LcqvnZn51tUgZDrymBO3vOgcQujT/e8f2w7W3Y+jbs/JA0Xxm7tQuf9rmDpycO5dwBnYiNOnNxt0uxxrjr1DDFdkbfeNvegeR05z74cOrUHy6413ldWQa5q52iv+dTWDPPeZALoGO/U0W/52TnGkBtVCFvM2xdCFsWQW5gyIb2vZz7/wdNp1faRP7kbZ3/lMa0RlV3vlmhb4rifCg95hTJME2++wVRsc5TuekTgbvBVwn71zpFf8+nsPF1504egHbpkDoeOg+GlEHO5+P7nLP2LQvh2B5nux5jYerPnDuLUgY1+Gtz61thjKnh5F034flP2boKvd8P7//cGZ4YnO6btk0cCbO5eKMgdazzMeX7zr34Bzc6RX/3J5C9HDb8q0abWOdi8jl3woBLIKmbO9mNMc3Kum6aYtU/4L+PwagbYOJ3I6fIB+PxQrcRzsfEW51lZYVwaCsc2gKxSdB3qjPaZiOUVviI8XrCdsZgjKm/k8MUe8JTgltXof/v45A2EWY+0TL7KWITITXD+Wik5TsP89yyPSxcv58bJ/bk11d+8aExezDWGJf5q26vtK6bhis54nR1tMQiX82xE+UkxkXjbeAPwb9X7+MH89eQGOv8sx48XnraeqFlf1+MaTVOjl5pY900XNcRzkxSLXTSDb9fufbJZYz61Xtc+cSnFJRU1LvtfzYf5O5X1jKxTwde+o4zNHOs3UNvTEQKdx996yr0o2+Eo7vh88VuJ2kUBVbsOgLA+n0FPPYfZy6X0goft89bzbQ/fsjm/cdZsesIBScqeHNtLu9uPMCz/93NLc9nMaR7EnO/nsGALolk9GzPuxsP8NqqHBe/ImNMMKf66O2BqYYbMhM+fBDe+D7cnumMN9+CeD3CTy8bzP0LNwPw9Ce7GNOzPW+ty2XR+gMAXPpo8MlEJvftyF9mjyUpLhqAJ28cy83PZXLXy2vpnBjHOf07UVTmDI36zKe7qPT7mTGiexi+KmPMF5zso7dC33BRMfCVufD0VFgxF877kduJGiw+5vQ/5b77wiqiPMJ3zu/DtCFdWJ9TQGJcNNlHTzC0ezLx0V7axkUxokfyaRd2OraNZd6ciUz+7RL++P42Fq7PZd6KbMD5q2HFriP06pjAsB4t65ehMa2DDVPcNKljnSdOl/25xRV6n1/5yQJnHP2Nv7yY9zcfpFfHBAZ2TTw5Zs3YnvUfXjg2ysvh4nIOF5eTtecoN07syai0dvzwlbUADboGYIxpPuoP76BmrauPvkqEzwRVm025xwH40cUDSYiNYuaoHoxMa9dsA5PdPW0gV49NPfm+qivHGBNmVX30YboTrt6FXkS8IrJaRN4KvB8lIp+JyBoRyRSR8bW0u0REtorIdhG5t7mC18rvhx0fNG3SEZe8vmYfAFeO7tFs+/z2Ob1Pvj5W4oxD/+BVw7lhQjojUq3bxhh3BB5mCVPXTUOOcgewudr7h4Bfquoo4OeB96cR5ybRJ4BLgSHALBEZ0ui09VFxAgpznadKW5iXVmYT5RF6tItvtn3++NJBJ1+vzSkA4Prx6TzwleF0S26+4xhjGkDDezG2XkcRkVTgMuDpaosVSAq8TgZygzQdD2xX1Z2qWg68BMxsfNx68AVmT/LGhvQwoVBUVsno9HbNus9or4d37zyPq0b3YHRa8+7bGNM4VX30kTYe/SPAPUBitWU/ABaLyB9wfmFM/mIzegDZ1d7nABManLIhivOdzwkRPM5NEDlHTwDQIeGLk3o31YAuifzfdaOafb/GmKaRMD3FX+evExGZAeSpas3pmm4F7lTVNOBO4G/BmgdZFnSkFRGZE+jrzzx06FBdsermb1l3lFSNqNw1Kc7tKMaYMNEwDTxVn78bpgBXiMhunK6XqSLyPHAT8Fpgm1dwumlqygGqz6qRSvAuHlR1rqpmqGpGSkoTzsY79IHE7s4olkV5jd9PmKV1aMNVo1N5dtkebn0+i9IKn9uRjDEhEq4z+Sp1FnpVvU9VU1W1F3A9sERVZ+MU7KqZt6cCnwdpvhLoLyK9RSQm0P6NZkleG28UXPEY5G2CJ89rUUM1/nLmUHp3SuDtDQf48avr8PtbTnZjTMNVDYUQak25EnAz8LCIrAV+A8wBEJHuIrIIQFUrgduAxTh37LysqhubFrke+l8Eo2ef6q9vIdrGRrHkh+dz/bg0Xl+Ty/l/+ACfFXtjWp9IO6OvTlWXquqMwOtPVHWsqo5U1QlVffiqmquq06u1WaSqA1S1r6o+0Lzxz6DnFKeffnNo/4BobiLCb68azlVjepB9pIS5H+10O5IxJkQiqY++ZRp6lTPH6qs3Q06m22nq7ZPP83lu2R5GprYD4G+f7KSk3PrrjWldwntG3/rGuqkSHQez5sOjIyHrH02atSlc8gpL+cbfV1BZrbsmv6icR97fxn3TB7uYzBgTCnZG3xzapjhDF294DbJXuJ2mTq9k5lDpV17/3hT+Onss3ZPjSEmM5Zz+ndyOZoxpTmHuo2+9Z/RVLvx/kP0ZPHsFfPUZGDS97jYu8PuVl1buZWKfDoxMa8fINLhkWFe3YxljQsrO6JtHuzT41rvQeTDMvwF2f+p2oqA+3p5P9pESvjahZY68aYxpiMAZfZjuqmv9hR6cLpyb3oB2PeG1OXDiiNuJvuDF5XvokBDDxUO7uB3FGBNqkXx7ZYsWm+h03RQdhIV3uZ3mNAePl/L+5jyuGZtKbJRN6G3M2UKt6yYEeoyB8+6GjQtg85tupzlp/spsfH5l1vh0t6MYY8LCzuhDa/L3nfvrF9wCR3a5nQafX3lpxV6m9OtIr04JbscxxoSR3V4ZKjFt4JpnnTFwltzvdho+3JZHbkEpXxtvF2GNOVtE3KBmrVK7NOj/ZdjwL8he6WqUF5fvpVPbWL48xC7CGnO2aQmDmrVs5cUQ3x469XctQu6xEpZsyePajFRios7efwpjzjp2Rh8GpQWw/T3I+BbEt3MtxvyV2SjYRVhjzlLWRx9KZUXO56TurkWo9PmZvzKbc/unkNahjWs5jDFuCJzRW6EPocRukNQDVr8AFaWuRFiyJY8Dx0v5mp3NG3PWsYux4eDxwLT7IXcV/GUyvHE7vHgdHFgftggvrthL58RYvjS4c9iOaYyJLOG6GNv6BzWrzbCrnKdlP37YeXiq5Ch0GwVdh4f80NlHTvDhtkPcdmE/or1n5+9aY85maqNXhlH/LzsfAH8cDoe3h+Ww81dmA3DduLQ6tjTGtGbhmtLaTier9BgNOz+A8hMhPUyFz8/8zGwuGJBCanu7CGvM2cn66N0x8mtw4rDTbx9C/9l8kEOFZdxgwxEbY8J0Sl/vrhsR8QKZwD5VnSEi84GBgdXtgGOqOipIu91AIeADKlU1Muf06z7a+XxgPfQ6J2SHeWH5Xrolx3HBwJSQHcMYE9nCfddNQ/ro7wA2A0kAqnpd1QoReRgoOEPbC1U1v1EJwyWxC7TtAvvXNXlXb67N5fZ5qwEYkZpMavt4BCHn6AnW5hTw5SFdiLKLsMac9cL1wFS9Cr2IpAKXAQ8Ad9VYJ8C1wNRmTxduXYc3yy2Wy3cdPvl6XU4B63Kc34FtYpyx5meM6NbkYxhjWrKqM/rIur3yEeAeIDHIunOBg6r6eS1tFXhXRBR4UlXnBttIROYAcwDS0116iKjrcNj5IVSWQ1RMo3dT6Qv+W/ovs8dy/gDrsjHmrBfoutFImUpQRGYAeaqaVcsms4B5Z9jFFFUdA1wKfE9Ezgu2karOVdUMVc1ISXGpGHpjwF/hfDSSqrJs5+Gg61btOdro/RpjWo+qPvpwzTBVnzP6KcAVIjIdiAOSROR5VZ0tIlHAVcDY2hqram7gc56ILADGAx81PXoIqAIC0vj+cxHhvTvP56Nth9i8/zj5RWU8u2wPAN86p3czBTXGtGxVY91EyDDFqnqfqqaqai/gemCJqs4OrL4I2KKqOcHaikiCiCRWvQamARuaJXko9JwEKGx/v0m7iYnycNGQLpw3IIV5K7OZ3Lcj2x+4lOT46ObJaYxp2arO6FvIoGbXU6PbRkS6i8iiwNsuwCcishZYASxU1XeaeMzQqShptl0dKizjO//MIqVtLI9/bYzdZWOMOeVkoQ/P4Ro0BIKqLgWWVnv/jSDb5ALTA693AiObEjCs9mU53TYDLm3Sbsor/XzvhVUcKynn1Vsn0yGh8Rd2jTGtkERY183ZR5p0MRbg/oWbWLH7CL+7egRDuyc3Uy5jTGshNgSCi3pOBvXBro8bvYuXM7N5btkebj63NzNH9WjGcMYY0zhW6KvrdS7EtIXNrzeq+ZrsY/x0wQam9OvIjy8Z1MzhjDGmcazQVxcV64xTv/5VOLixQU0PFZZxyz+zSEmM5bFZdvHVGBM5rBrVdO7dEN8e/nEZHMuuV5PqF1/nfn2sXXw1xkQUK/Q1te8J1/zdmXEqZ2W9mnz3hSxW7D7CjRN72sVXY0zEsUIfTJehznAIu+u+KLt44wHe35wHwMJ1+0OdzBhjGswKfTCxiTB6NmT+HT76wxmfakiKi2Z0ejv6dEogt6CUlbuPhDGoMcbUzQp9babd78wnu+TXsG1xrZtN6tuRBd+dwo2TnBmj/u/dbeFKaIwx9WKFvjYxCXDtP50unD2f1Ln5oK5JALWOXGmMMW6xQn8m0XHQfQzsrrvQx0Q538ofXTywji2NMSa8rNDXpe9UyF0DxWeeCTEmcN98ko1QaYyJMFbo69L/y4DC1rfPuFmX5Fi8HuFfmdn4wzRrjDHG1IcV+rp0Hw1JPeoco75NTBQ+v7I2p4ByX3hGpDPGmPqwQl8XEUgbDzmZZ9xs+qPOPfcDurQlLtobjmTGGFMvVujrI20iHM+BgqATaXHsRDl7j5wA4P+uHRXGYMYYUzcr9PWRNt75vP6VoKs/+ty5UHtu/04M62FDIBhjIosV+vroMsz5vPezoKu/P281AL+aOSxciYwxpt6s0NdHVAzEJEKHPmfc7JlPdoUpkDGmVWghk4OfPeLbQcmxoKvunjYAgH9+tid8eYwxLZdE6FSCIuIVkdUi8lbg/XwRWRP42C0ia2ppd4mIbBWR7SJybzPlDr+4ZCgtAKDC56eorJKySh+lFT4+3e4Me7DsvqluJjTGmKCiGrDtHcBmIAlAVa+rWiEiDwMFNRuIiBd4AvgykAOsFJE3VHVTU0K7Ii4ZSo8BcPljn7DlQOFpq0eltaNbcrwLwYwx5szqVehFJBW4DHgAuKvGOgGuBYKdzo4HtqvqzsC2LwEzgZZX6FUhfyuqyraDhZzTrxOT+nZEVUmMi2Z87w5uJzTGmKDqe0b/CHAPkBhk3bnAQVX9PMi6HkD1+fhygAnBDiAic4A5AOnp6fWMFUbH90GnARSVVeJXOH9ACjefd+aLs8YYEwnq7KMXkRlAnqpm1bLJLGBebc2DLAt6mVlV56pqhqpmpKSk1BUr/PyV0L4XBSUVACTb4GXGmBaiPmf0U4ArRGQ6EAckicjzqjpbRKKAq4CxtbTNAdKqvU8FcpsS2DUlx6BNx5OFPim+IZc3jDHGPXWe0avqfaqaqqq9gOuBJao6O7D6ImCLqgYfGwBWAv1FpLeIxATav9EMucMvOg7Ki1mb7VxzXr33mLt5jDGmnpp6H/311Oi2EZHuIrIIQFUrgduAxTh37LysqhubeEx3JPWAY3sY2t2ZSerJj3ayYHVtv9+MMSZyNKjQq+pSVZ1R7f03VPWvNbbJVdXp1d4vUtUBqtpXVR9oemSXpE+C3Z8yMj6fv84eA8Dv3t7qcihjjKmbPRlbXxNvgdhEeHYGl/RP5OczhnDgeCm784vdTmaMMWdkhb6+OvSBrz4Dhfth6yK+NLgzAEu35rkczBhjzswKfUP0Ohfi2sGeT+nZMYHuyXGssouyxpgIZ4W+ITwe6DYS9q8FYEj3ZDbtP+5yKGOMOTMr9A3VbQTkrubDDXvIKyxle14RJeU+t1MZY0ytrNA3UEnHoQC8M+9R1uU499QfPVHuZiRjjDkje7yzgWTw5fDmLYzrDNddNYV28dF0b2ejVhpjIpcV+gaKjWuDT4VubfyMSmvndhxjjKmTdd00kHg8HJKOxBzf7XYUY0wLp8HHeGx2VugbYV/icHoUrnc7hjGmxQpv6bVC3wgV3TLoSj4Hc3a4HcUYY+pkhb4ROgw6F4CcdR+5nMQYY+pmhb4R0gc5w++XH2h5MyIaY84+Vugb4fjRQwB42kbgTFjGGFODFfpGSOrgDGjmP3HE5STGGFM3K/SNEBefQKHGI8X5bkcxxpg6WaFvpGOe9kSXWqE3xkQ+K/SNVOxNJrb8qNsxjDGmTlboG6k0Opn4ygK3YxhjTJ3qXehFxCsiq0XkrWrLbheRrSKyUUQeqqXdbhFZLyJrRCSzOUJHgsqoBGL9JW7HMMaYOjVkULM7gM1AEoCIXAjMBEaoapmIdD5D2wtVtVV1aPuj4onVMrdjGGNMnep1Ri8iqcBlwNPVFt8KPKjqVDtVPasmT/VHxROnpW7HMMaYOtW36+YR4B7AX23ZAOBcEVkuIh+KyLha2irwrohkicic2g4gInNEJFNEMg8dOlTPWO7R6DbEYROOGGMiX52FXkRmAHmqmlVjVRTQHpgI/Ah4WUQkyC6mqOoY4FLgeyJyXrDjqOpcVc1Q1YyUlBbwxGl0PNHio7zMzuqNMZGtPmf0U4ArRGQ38BIwVUSeB3KA19SxAudsv1PNxqqaG/icBywAxjdTdldJTAIAJSeKXE5ijDFnVmehV9X7VDVVVXsB1wNLVHU28G9gKoCIDABigNMuuIpIgogkVr0GpgEbmvMLcIvEtAGg7EShy0mMMebMmnIf/TNAHxHZgHOmf5Oqqoh0F5FFgW26AJ+IyFpgBbBQVd9pWuTIIB4vAH5fpctJjDHmzBo0Z6yqLgWWBl6XA7ODbJMLTA+83gmMbGrISKSVzoXYqJg4l5MYY1oqVZtKMKJppXMPfXRsvMtJjDEtTfn+jQC075wWluNZoW8kLS8GIDbOCr0xpv6O5O1j5N7nWBeXQbtOXcNyTCv0jRR95HMOkEJcfILbUYwxLciR/btoI2WUxaVQcPhgWI5phb6ROhVv40Cbfm7HMMa0MD0Hj2Nd3FjGHXubXU9/PSzHtELfCKUlxaT69lHSYbDbUYwxLUx0TCwj7l3Cqrbn07NkE4dyd4f8mFboG2F75vtEiZ/43hNOLlv25O0c/UUqe7aucS+YMabFiJl0Mwl6gti5kzl8MCekx7JC3wiF2z7Gr0L/8ZecXJZ64H3aU4jOv5ENn7xxxvZFx4+yb+fGM25z9NB+shb9jS3L30X9/jNua4xpeYZNuZysPreQRDH52dtCeqwG3UdvHJ6Sw3hEKX54NJWUEavlpEkFAB38h0l/7+usOPgLxl/9g5NtfJWV7Fj3CUc2fcDA7c/Qg+OsixtLSZ9L8OaswOMvoyKmHYiHNkV7GFi6nrHiPIz12eobmHjLn134So0xodRu0Hn4dj5B8dI/QsbUkB3HCn0jdDz322T+pxC/Nwb1xuGPjoeoeDqOnkHawLFseOwrjF//vyzPXY12GgDH9tL34GIG4Ew9uDFmJFu6TmTQ3nm035TFYZIp8iSRUFRIFJUc8nZlVeev0G7CLI5/+gwTD7xA5lujyJhR6+CfxpgWaPCEi1m29kYm5T7H0UP7aZ/SLSTHkXA9mdUQGRkZmpnZciejKis9wYYnbmD48Q+JER/lGsXGhAn4hlxJtyFT6N5rMOLxcKKogMP7d5PadzjiCd6LVlFexo7fX0Bq+S6i79tJbFybMH81xphQWvP+PEZ9cgtbZyxgYBPO6kUkS1Uzgq2zM/oQiI1rw9gfLsDv83Hk8AGiY+MZndzhC9u1aZtMm/5nHiEiOiaWo2kXMWjnnyjx+UIV2RjjEn9F6Ic6t0IfQh6vlw6dezR9R+VF+FTO+HCW3+dj1cK5eLe+RXlsB6THWMbOvA1vlP0TGxPJVJ2bLY6sXRSyfnq766Yl8MbgFWX14meDrt607G12/GY8GavupVvxFgYcWcr49f/L1ocuYP+erWEOa4xpiDGXfJOsxKlMyn6KNe/PC8kxrI++BSg6fpTsx6bTv3wLK9P/h7b9zyU60Fdf/PGfGVu4hIN0JHvMjxhz2RxEhMw3/syQ1b/Gj7BtwgOMnf4/Ln8VxpjaZH++lrQXziMz6SIy7nq1UfuwPvoWrm1Se9JuX8SGv85iUvZTkP3UyXVlGs2ynnMYdd3PyUhIPLl83JW3sW/Elyh68RuMXXEXqzYuoP+c50gMcq3AGOOu/Rs+JA2IGfO1kOzfzuhbEPX72b0li5KCfCrLTuCvKKPLgLF06zmw1jYV5WVkvvBzMnY/xa7ovnS59S2SO3YJY2pjTF223T+OjpUHafOjjcRXO2FrCDujbyXE46H3kHENahMdE8ukb/6ONe+PYsjHt3HssYlkjvkxY2fMqfWWTmNM+Kxe/CyjK7exrOctTGpkka+L/U8/S4y6aBa7Ln+F4972ZKz6MVt+ew6FBUfcjmXMWe3oof2M/O8dAKSfH7qRLK3Qn0UGZkylz/9byYph/8vgio1smP8LtyMZc1ZLSGrPQekEQPan80N2HCv0ZxmP18v4r97FDm8fxu97zu04xpzVYmLjSL47i40xIxiy42n8IXoost6FXkS8IrJaRN6qtux2EdkqIhtF5KFa2l0S2Ga7iNzbHKFN0x3qPROvKLm7trgdxZizWpu2yRQPvpYkitn46ZlHvm2shpzR3wFsrnojIhcCM4ERqjoU+EPNBiLiBZ4ALgWGALNEZEiTEptmkT7leoo1jiMvfcftKMac9fpMmglA8ab3QrL/et11IyKpwGXAA8BdgcW3Ag+qahmAquYFaToe2K6qOwP7eQnnl8OmJuY2TdS99yA+638rE7f/kR3r/kvfEZPdjmRauCN5+8ifeyUplQfw4ENQPKp48Ac+FKh+O7ecfKWB19XXnlom1KRnaKtSffv67vfUdk5SqfYRSC5VX0FgvTjrKiSGSk8sUf4yorWsWhsJtKn2Gc8XlvnFi98TQyega95HZ/oWN1p9b698BLgHqH7vzwDgXBF5ACgF7lbVlTXa9QCyq73PASYQhIjMAeYApKen1zOWaYrB079HyaNPcOy9h2DEv92OY1qwkuJCcp65iUEVO1ndaQZ4olDxgHgAAfGgHi+ninu10nvy5akJdiTY8z0abAKewHYnt6++X+e1BFl2eumvuX9F1O+8V6c8O+sCy+HkesGPx19BlK+Esqi2+Lzxzr61alvns+APfE3+wPvAcvUT7a8gqvI4AAfbj6FXkK+yqeos9CIyA8hT1SwRuaBG2/bARGAc8LKI9NHTn8D64q/ioN9hUNW5wFxwHpiqV3rTJMkdUljW8xtM2juXzDf+QsYVt7odybQwlRXlZL78W/p9/gzDtICVw37GhGt+6HasFivoWXAzqM8Z/RTgChGZDsQBSSLyPM7Z+WuBwr5CRPxAJ+BQtbY5QFq196lAbrMkN81i/Nd/y97fLCR606tghd5Uc6KogH2fr+XYnvVIdAxpI6fSJbUv4IyWumnZQqKW/oaJlZtZHzuGvAt/zISJl9SxV+OGOgu9qt4H3AcQOKO/W1Vni8gtwFRgqYgMAGKA/BrNVwL9RaQ3sA+4HgjNYA6mUbxRUeQlDKJH4Tq3o5gwKDh8kM2Ln4KCHNIv+QHdew9ylh/NJz97G0WH91Gav5eYXe8ztHgF/QPTWQKwEg7SkXKJpZM/n2FSzjHakpnxe5v9LMI1ZQiEZ4BnRGQDUA7cpKoqIt2Bp1V1uqpWishtwGLACzyjqmeeFduEXWz5UYq9SW7HMCG2f89W9B8zmKh5VKgX/cfLrIsfjc8Tw7Di5fSVU/dwH6Qjq7pcTUzfc+nYazgVpUXkb1xK1IE1ePxl7Es4n6j0sQy7cBYZbdq6+FWZ+mhQoVfVpcDSwOtyYHaQbXKB6dXeLwIWNSWkCa0TbdPok7+JkuLCRg+oZCJbRXkZxc/NorMWseXSV+iYNoCdr/2KjkfX0NZXQFbXa4jpPYk2HXqQlJJG1/T+dPF6T9tHv5HnuJTeNJUNamZIzJhFwuLXWb7oSSZcc7fbcUwIZD13HxN9O1g16XHGTJgGQMptz5xc39WtYCYsrNAbBk+4mL3v9SBuxzuAFfqWrPREEQf2bMEbHcvRnG2cyN2EJ38rGYcXsrLdJYy7+Ea3IxoXWKE3iMeDR/34vbFuRzENVFlRzt6tq8lbvZCEnI8YULqBXlIBnLrdrYAENrYZx8Bv/tm9oMZVVugNB7K3k6r7yelxg9tRTD3s37OV7AW/oHvBarr48+gjPvoAuz3prO56NVGpY1BfBQld+tKlz3A6pHRnpM09cFazQm8oLToGgETHuxvEnNHxY4fZOP8XjMmdR3tgU+IkspOmEdV5AD3HXUavHr1D8lSlafms0BvS+o9ih7cPvTc+wYlLb6ZN22S3I5mA0hNFbF+1hMJN7zEodwGTKGRlu4tJ++pvGJPWz+14poWwQm/wRkVRcsEv6Pufr5P5wUtkXG4jWrqt6PhRNrxwL6MPvMowqcCnwvo2Ezh8yc8YZ7c5mgayQm8AGDJ5BsXvx1G5dyVghb6xSk8UsfGDl6gsPkJshzQGnzOT2Lg2DdqH3+djx1+uZfyJlWS2v5TYEV+hz9iLGJXcIUSpTWtnhd4AzsxTxzzJRJcedjtKg6nfz97P14HfR9qAUXhqPOgTLjnbN1A2bzZjfbtOLjvyyT3s++q/6DOs7uGq/D4f+Qf2svPtx5hYsoLlQ+5jwnU2V49pOiv0BnCenOzoP0J2m85uRznJV1lJaUkRCYntTlteWVFOWekJdq//L4Vr3yA17wN66gEA8ujAoWlPMHTyyYezUb+fysoKomNCc/toWekJ1r79Nwat/S0KrJr0OOkjzyd38wq6fHgPif+6jpy4t0jtN+y0dkXHj7Ij631KN75NlyMr6OY7QGepoDOQlXgh46+5JyR5zdnHCr0BIGf7OnpLBVGpY9yOwsGcHex68/f0PfgOKRxljyeVvKTh+Dr0Jyp/E0MKPiZByhgKlGsUm+NHs6/P/yDR8XRd/xcGLr6BtR9nUJJ6LvHZHzG4ZBUelC3RA/FMf5ABYy5olpz5uXv4/O0/MSD7FcZTwHZvX9rMfpExgYHCOnVNZ3fXXiTP/wr6/OUs63EFnrJCoksP0f7EHnr69jJSlBKNYVv8SA60Ow9p35P4Lv0YOeVyxG6JNM1ENNgA/y7LyMjQzMxMt2OcVda89yKjPr2VrTMWMDBjqms5srevJ+b5mbTXY2xMmEBpp2HE56+n54mNtOc4BSSwtcNU/O37EJPShwGTZ9I2qf3J9oUFR9jw0s/oeeBdumseR0lia+dLUW8svfcvopMeYVP8GEr7XcbAC28guWOXBmfctmopx5c+xoiCD4jCz7o2E/BO+i7DzglenHes/wz/67fRv/JzjpPAUU97jsV040Tn0bTtN5n+46YRF5/QpO+bMSKSpaoZwdbZGb0BIK69U/CK8nbVsWVolc+7kY5aRPZXFzF6+MSTy9Xvp7DwGG0TkhgfVfuPbWJyByZ95wnU7+fwoVzaJndgYuBi6LH8n7Dy1QdIO/AuqRt+Sc7GJ4n9URZxQUZfLC0pRkQoPJbPwZ0bGDzhYo7m7+fg09cxpGIDRRrPqi5XkzrtDkbV6JKpqe/wiTA8k4ryMpJiYkkCejbu22NMo9gZvQGcfu/jD/TFh5ddw77PqMu/S0xsXNiOv/LfjxO/+RWGla1hWfp3mPSth0J2LPX7Wf3eC4xZdhsrhv+S8Vf/AIDMN/5Cz1W/I1kLiZFKyjUKD36ixM+mmOG0rTxCiu8QawfewdDLvkui3QVjIsiZzuit0JuTdm5Yjm/Bd+nv287m6KEkf+2ZkxNThIqvspKVL/+Widv+wHHasLH3txj3tf8lKjompMdVv5/Dv+rNruQJjLvzZdYueYlhH97C9ugBHEmZADFtkLJCNCoWotswcudT+PCwe+qfGX7+VSHNZkxjWKE39aZ+P1lvzWVo1s+IppL1CZMoS0wnqsdIek+4nI5dUpvlODs3LOfo4gfpV7icZIpZFzeOAd//d9BulFDZ+auRJPmPsb3LpUw8OI+jJBLzww1fuMsHnDtroqJi8J6h28gYN1mhNw2Wt28XO9/8Pb0OLKadFhAnFfhV2Bw7nOKBVzNw6myS23dq0D4LDh8E8bBpwYNk7P07JRLHlnbnEzVwGqMvvinsd5msePWPjF73a6LFR450o+SKufQffV5YMxjTXKzQmybx+3zsWPcp+avfJDX7LdI0lzKNZlX6N+j5pZvJXvkWkrsKT+9zGD395tPOetXvZ/m8X5O+/Xm6a97J5ZlJF9Hv60/QrpO7U16UnigiOibOztRNi2eF3jQb9fv5fM1HFH7wKGMLl5xcXqrRxEkFy3p8k0k3PwLA5uWLqfjgd4wozWJD7CiKUs8HVdr2zmDYuTNd+gqMaZ2apdCLiBfIBPap6gwR+QVwM3AosMn/C8wPW7PdbqAQ8AGVtQWpzgp9y7B5+WKO715N+/4T6TfyXLL+NItxBYs5ShLlRNOFw8597APmMOH6n9gDQMaEUHMV+ruADCCpWqEvUtU/1NFuN5Chqvn1DWyFvmUqKS5k3ZuPQ94mvJUlVHYbw8grbrcJx40JgyY/MCUiqcBlwAPAXc2YzbQi8QmJTLj+PrdjGGNqqO/f0o8A9wD+GstvE5F1IvKMiLT/YjMAFHhXRLJEZE5tBxCROSKSKSKZhw4dqm0zY4wxDVRnoReRGUCeqmbVWPUXoC8wCtgPPFzLLqao6hjgUuB7IhL0/jVVnauqGaqakZKSUt/8xhhj6lCfM/opwBWBvvaXgKki8ryqHlRVn6r6gaeA8cEaq2pu4HMesKC27YwxxoRGnYVeVe9T1VRV7QVcDyxR1dki0q3aZl8BNtRsKyIJIpJY9RqYFmw7Y4wxodOUp0QeEpFROH3wuwnMPyci3YGnVXU60AVYICJVx3pRVd9pSmBjjDEN06BCr6pLgaWB1zfWsk0uMD3weicwskkJjTHGNIk9wWKMMa2cFXpjjGnlInKsGxE5BOypY7NOQL2ftg2jSM0FkZvNcjVcpGaL1FwQudmaK1dPVQ16b3pEFvr6EJHM+oybE26RmgsiN5vlarhIzRapuSBys4Ujl3XdGGNMK2eF3hhjWrmWXOjnuh2gFpGaCyI3m+VquEjNFqm5IHKzhTxXi+2jN8YYUz8t+YzeGGNMPVihN8aYVi7iC72IXCMiG0XELyIZ1ZZ/OTDG/frA56nV1i0Vka0isibw0TlSslXb5g0RCckAb438nr0jImsD7f4amDrS1Vwi0kZEForIlkC7B5s7U2OzBdY9ICLZIlIUYbnGBpZvF5E/SWCwqTBm6ygiH4hIkYg8XqPNdYE5LDaKyEMRlGtW4Hu2LvB/oZPbuUQksVoNWyMi+SLySKMOrqoR/QEMBgbijLGTUW35aKB74PUwnLlsq9adtm0kZQssuwp4EdgQKblwpogEEOBV4Hq3cwFtgAsDr2OAj4FLI+h7NhHohjOlZsT8jAErgEmBf8u3XfieJQDnALcAj1db3hHYC6QE3j8LfCkCckUBeUCnwPuHgF+4nStI+yzgvMYcuymjV4aFqm4GqHlSoqqrq73dCMSJSKyqlkVyNhFpizMd4xzg5UjJparHA8ujcIpqs1+lb0SuE8AHgW3KRWQVkNrcuRqZrUxVPwvWxs1cQAecX9rLAu2eA67EKfjhylYMfCIi/Wo06QNsU9WqKeTeB64G/uNyLgl8JIjIYSAJ2N6cmRqZ61RAkf5AZ5yTnQaL+K6beroaWF2jyP898OfOz0L1p2s91cz2a5zZuE64FwkI8j0TkcU4ZzaFwL8iJReAiLQDLqeZi0IDBc0WAarn6gHkVFuXE1gWCbYDg0Skl4hE4fwCSnM3EqhqBXArsB7IBYYAf3M11BfNAuZr4NS+oSLijF5E3ge6Bln1E1V9vY62Q4Hf4UxqUuUGVd0nzqQnrwI3As+5nU2c8fv7qeqdItKrMXlCkauKql4sInHAC8BU4L1IyBUoCvOAP6kz9HWjhCJbc2jmXMFOahr911lTsn0hhOpREbkVmI8z//R/cc7yXc0lItE4hX40sBN4DLgPuN/NXDVcj1PHGiUiCr2qXtSYdiKSijM94ddVdUe1/e0LfC4UkRdxpi9sVKFv5myTgLHiTMsYBXQWkaWqeoHLuarvt1RE3gBm0ohCH6Jcc4HPVfWRxuw7xNmarJlz5XB691YqzllqWLOdYX9vAm8CiMgcwBcBuUYF9rkDQEReBu5tzI6a+/sFICIjgSj94rzd9dZiu24Cf8ovBO5T1U+rLY+qumIe+E09gzBPX1hbNlX9i6p2V2daxnNw+isvcDuXiLSVwNSQgbPn6cAWt3MF1t0PJAM/CFeeGsdvRy3Z3HSGn7H9QKGITAx0WX4daMqZZLOSwB1wItIe+C7wtLuJANgHDBGRqpEfvwxsdjFPTbNw/qJtvKZeSQ71B858tDlAGXAQWBxY/lOgGFhT7aMzzhXsLGAdzkWqRwFvJGSr0bYXobvrpqHfsy7Aymrfs8dwziDczpWK0+2wudryb0fC9yyw7qFAG3/g8y8iJFcGzsnNDuBxAk/AhytbYN1u4AhQFNhmSGD5PGBT4KPZ7+xqQq5bAj9n63D+4ugYCbkC63YCg5pybBsCwRhjWrkW23VjjDGmfqzQG2NMK2eF3hhjWjkr9MYY08pZoTfGmFbOCr0xxrRyVuiNMaaV+/9t6IHU05hzKAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAP = tractMAP  #committing to the trimmed map (for TN, using convex hull)\n",
    "#bufferDistance = 0.02  #do minor buffer for OH\n",
    "origMAP = wholeMAP    # let's keep a copy of the original map prior to buffering\n",
    "#MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "#MAP = MAP.union(MAPbuffer)  #not buffering for TN\n",
    "print(\"original and final map areas are\",origMAP.area,MAP.area)  #same if we didn't buffer\n",
    "plotPoly(tractMAP)\n",
    "plotPoly(origMAP)\n",
    "plt.show()\n",
    "\n",
    "minTractPop = 10  #was zero to not exclude empty interiors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "8e271f55-e118-4575-bc99-c28c59f435b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 population, Trump+Biden voters, lean =  7705266 3954190 0.4007449681520931\n",
      "compare to Census 7705281 Leip has Trump + Biden = 3954263.0 0.4007449681520931\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - these match 2020 census, USelectionatlas.org :-)\n",
    "censusPop = 7705281\n",
    "atlasTrump = 1584651.\n",
    "atlasBiden = 2369612.\n",
    "\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"compare to Census\",censusPop,\"Leip has Trump + Biden =\",atlasTrump+atlasBiden,\n",
    "      atlasTrump/(atlasTrump+atlasBiden) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6c47f717-f208-4a4c-b825-a3181a83c6ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state pop before, after slice operation are 7705266 7705266.0\n",
      "state Trumpers before, after slice opn are 1584614 1584614.0\n",
      "state Bideners before, after slice opn are 2369576 2369576.0\n"
     ]
    }
   ],
   "source": [
    "#Check on integrity after slicing\n",
    "sumPop = 0.\n",
    "sumTrump = 0.\n",
    "sumBiden = 0.\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] == 0:\n",
    "        sumPop += tractPop[t]\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] == 0:\n",
    "        sumTrump += vtdTrump[p]\n",
    "        sumBiden += vtdBiden[p]\n",
    "print(\"state pop before, after slice operation are\",np.sum(tractPop),sumPop)\n",
    "print(\"state Trumpers before, after slice opn are\",np.sum(vtdTrump),sumTrump)\n",
    "print(\"state Bideners before, after slice opn are\",np.sum(vtdBiden),sumBiden)\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] ==1 and tractPop[t] > 0:  #should not occur\n",
    "        print(\"missed pop for tract, pop, (x,y)\",t,tractPop[t],tractGeom[t].centroid.x,\n",
    "              tractGeom[t].centroid.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b5334311-b597-4568-88c7-eb3c9d4bdf09",
   "metadata": {},
   "outputs": [],
   "source": [
    "#FAIL here with a bad precinct when building grid map in Georgia.  See below two blocks for fix and re-run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c3eb2c10-5caa-42ce-a70f-d87d6e275e22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2034 1\n",
      "0 0.00016990724566590647\n",
      "1 2.9354445031118904e-11\n",
      "2 4.989851763673341e-17\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#WTF is going on with this precinct?\n",
    "print(p, notPolyVTD[p])\n",
    "counter = 0\n",
    "for geom in vtdGeom[p].geoms:\n",
    "    x,y = geom.exterior.xy\n",
    "    plt.plot(x,y)\n",
    "    print(counter,geom.area)\n",
    "    counter+=1\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2532d156-de55-4a64-ac38-1a373a9ab910",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#OK, kill the bad sub-polygons\n",
    "counter = 0\n",
    "for geom in vtdGeom[p].geoms:\n",
    "    if counter == 0:\n",
    "        goodGeom = geom\n",
    "    counter+=1\n",
    "vtdGeom[p] = goodGeom\n",
    "notPolyVTD[p] = 0\n",
    "x,y = vtdGeom[p].exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "2f6c72cd-90d8-489d-9b15-d4278ba17b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 105 grids for 10 districts\n",
      "starting grid no 0 at x = -123.94239786666667, y = 45.79060514285713 \n",
      "starting grid no 5 at x = -123.94239786666667, y = 48.261246571428565 \n",
      "starting grid no 10 at x = -123.45782360000001, y = 47.272989999999986 \n",
      "starting grid no 15 at x = -122.97324933333334, y = 46.284733428571435 \n",
      "starting grid no 20 at x = -122.97324933333334, y = 48.75537485714286 \n",
      "starting grid no 25 at x = -122.48867506666669, y = 47.76711828571428 \n",
      "starting grid no 30 at x = -122.00410080000002, y = 46.77886171428571 \n",
      "starting grid no 35 at x = -121.51952653333333, y = 45.79060514285713 \n",
      "starting grid no 40 at x = -121.51952653333333, y = 48.261246571428565 \n",
      "starting grid no 45 at x = -121.03495226666668, y = 47.272989999999986 \n",
      "starting grid no 50 at x = -120.55037800000001, y = 46.284733428571435 \n",
      "starting grid no 55 at x = -120.55037799999998, y = 48.75537485714286 \n",
      "starting grid no 60 at x = -120.06580373333334, y = 47.76711828571428 \n",
      "starting grid no 65 at x = -119.58122946666667, y = 46.77886171428571 \n",
      "starting grid no 70 at x = -119.0966552, y = 45.79060514285713 \n",
      "starting grid no 75 at x = -119.09665519999999, y = 48.261246571428565 \n",
      "starting grid no 80 at x = -118.61208093333333, y = 47.272989999999986 \n",
      "starting grid no 85 at x = -118.12750666666668, y = 46.284733428571435 \n",
      "starting grid no 90 at x = -118.12750666666666, y = 48.75537485714286 \n",
      "starting grid no 95 at x = -117.6429324, y = 47.76711828571428 \n",
      "starting grid no 100 at x = -117.15835813333334, y = 46.77886171428571 \n",
      "done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids = 328368.50663577503 5316728.3299013125 98.0\n"
     ]
    }
   ],
   "source": [
    "# this section - ESTIMATE POP'N DENSITY at scale of 1/3 of district length\n",
    "# also, IDENTIFY WHICH TRACTS and VTD's INTERSECT EACH GRID to speed later intersection searches by grid \n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 10   #WA congressional\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "#MAPMaxX = -80.   #In most state maps, we figure this out from the bounding box of the convex hull; see above.\n",
    "#MAPMaxY = 31.1   # ... but for Florida, we will do manually\n",
    "#MAPMinX = -87.0\n",
    "#MAPMinY = 25.5\n",
    "stateWidth = float(MAPMaxX - MAPMinX)\n",
    "stateHeight = float(MAPMaxY - MAPMinY)\n",
    "stateWHRatio = stateWidth / stateHeight\n",
    "G = 3  #adjustable parameter; G*G = number of grids that fit in an average district\n",
    "nGridsX = int( G*round((nDistricts*stateWHRatio)**0.5,0) )   #OK to have empty grids for a non-rectglr state\n",
    "nGridsY = int(round(nGridsX / stateWHRatio,0) )  #so grids are square even if state has high aspect ratio\n",
    "nGrids = int(nGridsX*nGridsY)\n",
    "print(\"map has a total of {0} grids for {1} districts\".format(nGrids,nDistricts) )\n",
    "nGridPrecincts = [0]*nGrids # will store how many precincts intersect with each grid square\n",
    "nGridTracts = [0]*nGrids    # same, but for tracts\n",
    "gridPrecinctNo = [[0]*nPrecincts for gridNo in range(nGrids) ] # initialize a list of VTDs that intersect with each grid square\n",
    "gridTractNo = [[0]*nTracts for gridNo in range(nGrids) ] # initialize a list of tracts that intersect with each grid square\n",
    "\n",
    "gridPop = [0.]*nGrids #will store approx total population for this grid square\n",
    "gridDensity = [0.]*nGrids\n",
    "gridGeom = [Polygon([(0,0),(0,1),(1,0)])]*nGrids  #initialize grid geometry\n",
    "gridWidth = stateWidth /nGridsX        #geo length of a grid's side length\n",
    "gridHeight = stateHeight /nGridsY    \n",
    "\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG/nGridsY)\n",
    "    y = int(nG % nGridsY)\n",
    "    point1 = Point(x*gridWidth+MAPMinX,y*gridHeight+MAPMinY)\n",
    "    point2 = Point((x+1)*gridWidth+MAPMinX, y*gridHeight+MAPMinY)\n",
    "    point3 = Point((x+1)*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    point4 = Point(x*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    gridGeom[nG] = Polygon([point1, point2, point3, point4])\n",
    "    if (nG %5 == 0): #print occasionally, should take about one second per grid\n",
    "        print(\"starting grid no {0} at x = {1}, y = {2} \".format(nG,gridGeom[nG].centroid.x,gridGeom[nG].centroid.y) )\n",
    "    counter = 0\n",
    "    #    Now for each grid square, find intersxn with all polygons, total the intersection population\n",
    "    #    Also, create 2 lists: of TRACTS and precincts that intersect each grid (efficiency shortcut)\n",
    "    \n",
    "    if( gridGeom[nG].intersection(MAP).area > 0.0001) : #don't bother w grids off the map\n",
    "        for t in range(nTracts):\n",
    "            # if (t%3000 == 0) :\n",
    "            #    print (\"grid {0}, tract no {1}, ({2},{3})\".format(nG,t,tractGeom[t].centroid.x,tractGeom[t].centroid.y) )\n",
    "            intersxnArea = 0.\n",
    "            if (notPoly[t] == 0) :  # tract is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(tractGeom[t]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #tract is a multiPolygon, do the polygon intersections individually\n",
    "                #print(\"I think tract\",t,\"is a multiPolygon\")\n",
    "                for geom in tractGeom[t].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            if (intersxnArea > 0 and tractPop[t] > minTractPop) : # this tract is at least partially in this grid and populated \n",
    "                if (tractArea[t] == 0):  #should not happen, but debugging\n",
    "                    print(t,tractGeom[t].centroid.x, tractGeom[t].centroid.y)  #debug print\n",
    "                gridPop[nG] += tractPop[t]*intersxnArea/tractArea[t]\n",
    "                if tractPop[t] > (1.0 * avgDistrictPop) :  #flag if we have a mega tract\n",
    "                    uhoh = input(\"Uh oh! We have a tract that's bigger than a district.  What now?\")\n",
    "                else :\n",
    "                    gridTractNo[nG][counter] = t  #add this tract to this grid's list, update the no of tracts in grid\n",
    "                    counter +=1            \n",
    "                    nGridTracts[nG] = counter\n",
    "        # OK, now, loop over PRECINCTS to develop each grid's list of precincts (prev loop was for tracts)\n",
    "        counter = 0\n",
    "        for p in range(nPrecincts):\n",
    "            intersxnArea = 0.\n",
    "            if (notPolyVTD[p] == 0) :  # precinct is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #precinct is a multiPolygon, do the polygon intersections individually\n",
    "                for geom in vtdGeom[p].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area\n",
    "            if (intersxnArea > 0 and vtdPop[p] >0) : # this precinct is at least partially in this grid and recorded votes                 \n",
    "                gridPrecinctNo[nG][counter] = p  #add this precinct to this grid's list, update the no of tracts in grid\n",
    "                counter +=1            \n",
    "                nGridPrecincts[nG] = counter\n",
    "    gridDensity[nG] = gridPop[nG] / gridGeom[nG].area  #finished loop over tracts & vtd's.  Calceach grid's population density    \n",
    "\n",
    "minGridPop = np.min(gridPop)\n",
    "# avgGridPop = np.mean(gridPop)   #see below for explicit averaging, since there are so many empty grids\n",
    "maxGridPop = np.max(gridPop)\n",
    "nPopulatedGrids = 0.\n",
    "totPopulatedGridArea = 0.\n",
    "for nG in range(nGrids) :\n",
    "    if gridPop[nG] > 0. :\n",
    "        nPopulatedGrids +=1\n",
    "        totPopulatedGridArea += gridGeom[nG].area\n",
    "avgGridPop = np.sum(tractPop) / nPopulatedGrids\n",
    "avgGridDensity = np.sum(tractPop) / totPopulatedGridArea\n",
    "#avgGridDensity = np.average(gridDensity) #avgGridPop / (gridPL * gridPL) #these densities help home district algorithm\n",
    "maxGridDensity = np.max(gridDensity)\n",
    "print(\"done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids =\",avgGridDensity,maxGridDensity,nPopulatedGrids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3f5a471b-867e-4db1-abdc-55f4b3d1f27e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7463 0.1998217504492339\n"
     ]
    }
   ],
   "source": [
    "print(p,vtdGeom[p].area)  #this will spit out the precinct number where we choked (again)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b538ed92-bf09-41ce-9758-8a764cbff950",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sigh, this precinct's geom is still whacked.  Convex-hull it\n",
    "# vtdGeom[2034] = vtdGeom[2034].convex_hull  #after this line, go back up and redo the grid code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "808f513f-21af-4b03-8061-fdeeed33dafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a heat map of grid density \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a heat map of grid density \")\n",
    "xyGridDensity = [[0.]*nGridsX for x in range (nGridsY) ]   #flipping x and y to get right orientation in pixel grid\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG % nGridsY)\n",
    "    y = int(nG / nGridsY)\n",
    "    if gridDensity[nG] > 0 :  #avoid log zero\n",
    "        xyGridDensity[x][y]=np.log(gridDensity[nG])\n",
    "c=plt.pcolormesh(xyGridDensity)\n",
    "plt.colorbar(c)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "4013f80b-4fc8-40a1-b708-ee29c29b3ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.500721050640116e-09 8.743921499978174e-06\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.min(vtdArea), np.min(tractArea))\n",
    "minTractPop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 8 3 500303.77522365586 3.0744\n",
      "8 yoyos for tract,wedge,wedgePop,r= 8 3 126645.10614799592 1.5372\n",
      "9 yoyos for tract,wedge,wedgePop,r= 8 3 500171.19810883066 3.0708\n",
      "10 yoyos for tract,wedge,wedgePop,r= 8 3 251141.20442686402 2.304\n",
      "11 yoyos for tract,wedge,wedgePop,r= 8 3 480754.31042792636 2.6874\n",
      "11 yoyos for tract,wedge,wedgePop,r= 8 3 401410.0078622646 2.4957\n",
      "12 yoyos for tract,wedge,wedgePop,r= 8 3 310372.8582220945 2.3999\n",
      "7 yoyos for tract,wedge,wedgePop,r= 11 2 115386.37676674894 1.094\n",
      "8 yoyos for tract,wedge,wedgePop,r= 11 2 7350.619693521177 0.547\n",
      "9 yoyos for tract,wedge,wedgePop,r= 11 2 104233.74101215384 0.9602\n",
      "9 yoyos for tract,wedge,wedgePop,r= 11 2 82071.32589856966 0.7536\n",
      "9 yoyos for tract,wedge,wedgePop,r= 11 2 68526.61915420229 0.6503\n",
      "10 yoyos for tract,wedge,wedgePop,r= 11 2 27170.882525922127 0.5987\n",
      "11 yoyos for tract,wedge,wedgePop,r= 11 2 49088.233403403385 0.6245\n",
      "12 yoyos for tract,wedge,wedgePop,r= 11 2 35415.51481616547 0.6116\n",
      "7 yoyos for tract,wedge,wedgePop,r= 15 3 369450.7962337958 2.0992\n",
      "8 yoyos for tract,wedge,wedgePop,r= 15 3 92184.60003890475 1.0496\n",
      "9 yoyos for tract,wedge,wedgePop,r= 15 3 369502.15899986844 2.1003\n",
      "10 yoyos for tract,wedge,wedgePop,r= 15 3 151459.2177113764 1.575\n",
      "11 yoyos for tract,wedge,wedgePop,r= 15 3 322496.53962129774 1.8377\n",
      "12 yoyos for tract,wedge,wedgePop,r= 15 3 206635.38670259446 1.7063\n",
      "12 yoyos for tract,wedge,wedgePop,r= 15 3 264465.7185002967 1.772\n",
      "I am working on tract number 20 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 25 2 67313.6535635547 0.8775\n",
      "8 yoyos for tract,wedge,wedgePop,r= 25 2 33030.50776354446 0.4387\n",
      "9 yoyos for tract,wedge,wedgePop,r= 25 2 61329.88256953455 0.7691\n",
      "9 yoyos for tract,wedge,wedgePop,r= 25 2 56407.94638863322 0.6039\n",
      "10 yoyos for tract,wedge,wedgePop,r= 25 2 35041.42546085086 0.5213\n",
      "10 yoyos for tract,wedge,wedgePop,r= 25 2 42141.32484909112 0.5626\n",
      "10 yoyos for tract,wedge,wedgePop,r= 25 2 47776.20484578162 0.5833\n",
      "I am working on tract number 40 of 1784 tracts\n",
      "I am working on tract number 60 of 1784 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 63\n",
      "we have 2 non-opposing shorted wedges for tract no 65\n",
      "we have 2 non-opposing shorted wedges for tract no 66\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 72 3.0 3 36.0 1.2883 525736.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 74 3.0 3 36.0 1.1439 469892.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 74 4.0 3 36.0 0.9609 469892.3\n",
      "we have 2 non-opposing shorted wedges for tract no 79\n",
      "I am working on tract number 80 of 1784 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 98 5.0 0 121.9 0.9601 342136.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 98 6.0 0 121.9 0.9175 342136.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 98 7.0 0 121.9 0.8768 342136.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 98 8.0 0 121.9 0.8379 342136.5\n",
      "I am working on tract number 100 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 114 2 119778.04988452353 1.1843\n",
      "7 yoyos for tract,wedge,wedgePop,r= 114 3 301307.74829381856 2.2618\n",
      "8 yoyos for tract,wedge,wedgePop,r= 114 2 34522.73422387212 0.5921\n",
      "8 yoyos for tract,wedge,wedgePop,r= 114 3 102075.03387992692 1.1309\n",
      "9 yoyos for tract,wedge,wedgePop,r= 114 2 136761.81610785535 1.227\n",
      "9 yoyos for tract,wedge,wedgePop,r= 114 3 377626.19413893414 2.2968\n",
      "10 yoyos for tract,wedge,wedgePop,r= 114 2 43441.53331412052 0.9096\n",
      "10 yoyos for tract,wedge,wedgePop,r= 114 3 119759.17572747477 1.7139\n",
      "10 yoyos for tract,wedge,wedgePop,r= 114 2 60697.181420024506 1.0683\n",
      "10 yoyos for tract,wedge,wedgePop,r= 114 3 154515.42991642695 2.0053\n",
      "10 yoyos for tract,wedge,wedgePop,r= 114 3 205656.20350577554 2.1511\n",
      "10 yoyos for tract,wedge,wedgePop,r= 114 3 253209.57628333318 2.2239\n",
      "loop31.0, tr114,wedgePops770393.98, 86024.6, 299538.3, 85976.6, 298854.4, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?011010 \n",
      "   targetWP, latest drx4 are tWP,dr, 299238.7,1.44, 299238.7,-0.0016, 86024.6,0.0794, 299238.7,0.0364\n",
      "I am working on tract number 120 of 1784 tracts\n",
      "I am working on tract number 140 of 1784 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 151\n",
      "we have 2 non-opposing shorted wedges for tract no 152\n",
      "I am working on tract number 160 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 169 2 298773.87769496895 2.519\n",
      "8 yoyos for tract,wedge,wedgePop,r= 169 2 47396.96537094994 1.2595\n",
      "9 yoyos for tract,wedge,wedgePop,r= 169 2 291155.07467304746 2.4694\n",
      "10 yoyos for tract,wedge,wedgePop,r= 169 2 102152.81368610772 1.8644\n",
      "11 yoyos for tract,wedge,wedgePop,r= 169 2 255470.50260297893 2.1669\n",
      "12 yoyos for tract,wedge,wedgePop,r= 169 2 176635.06163403616 2.0157\n",
      "13 yoyos for tract,wedge,wedgePop,r= 169 2 238693.0759996639 2.0913\n",
      "13 yoyos for tract,wedge,wedgePop,r= 169 2 212032.80718628745 2.0535\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 176 4.0 2 90.0 2.2239 194700.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 176 5.0 2 90.0 2.2062 194700.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 176 6.0 2 90.0 2.1885 194700.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 176 7.0 2 90.0 2.171 194700.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 176 8.0 2 90.0 2.1537 194700.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 179 5.0 3 90.0 3.7977 544740.3\n",
      "I am working on tract number 180 of 1784 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 180 13.0 3 36.0 5.6411 887420.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 181 1 20363.782450620085 1.6774\n",
      "8 yoyos for tract,wedge,wedgePop,r= 181 1 647247.6922632409 5.6983\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 181 21.0 1 51.3 3.6878 647247.7\n",
      "8 yoyos for tract,wedge,wedgePop,r= 181 1 647247.6922632409 3.6878\n",
      "8 yoyos for tract,wedge,wedgePop,r= 181 1 569035.7777676585 2.6826\n",
      "9 yoyos for tract,wedge,wedgePop,r= 181 1 79903.67925097747 2.18\n",
      "9 yoyos for tract,wedge,wedgePop,r= 181 1 153522.83251285076 2.4313\n",
      "10 yoyos for tract,wedge,wedgePop,r= 181 1 426636.88788547413 2.5569\n",
      "11 yoyos for tract,wedge,wedgePop,r= 181 1 297122.0982674265 2.4941\n",
      "12 yoyos for tract,wedge,wedgePop,r= 181 1 376049.4207363657 2.5255\n",
      "13 yoyos for tract,wedge,wedgePop,r= 181 1 337324.50948283414 2.5098\n",
      "14 yoyos for tract,wedge,wedgePop,r= 181 1 356288.65249076043 2.5177\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 186 2.0 0 90.0 0.2388 194194.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 186 3.0 0 90.0 0.2373 194194.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 186 4.0 0 90.0 0.2359 194194.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 186 5.0 0 90.0 0.2345 194194.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 186 6.0 0 90.0 0.233 194194.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 186 7.0 0 90.0 0.2316 194194.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 186 8.0 0 90.0 0.2302 194194.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 186 9.0 0 90.0 0.2288 194194.2\n",
      "I am working on tract number 200 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 204 1 688466.7062885186 3.9665\n",
      "8 yoyos for tract,wedge,wedgePop,r= 204 1 56753.07137422182 1.9833\n",
      "9 yoyos for tract,wedge,wedgePop,r= 204 1 695573.9002227644 4.1606\n",
      "10 yoyos for tract,wedge,wedgePop,r= 204 1 316432.51466578414 3.0719\n",
      "10 yoyos for tract,wedge,wedgePop,r= 204 1 556102.4871388822 3.6163\n",
      "11 yoyos for tract,wedge,wedgePop,r= 204 1 684524.7835911906 3.8884\n",
      "11 yoyos for tract,wedge,wedgePop,r= 204 1 672220.0996737946 3.7523\n",
      "11 yoyos for tract,wedge,wedgePop,r= 204 1 640613.9989909672 3.6843\n",
      "11 yoyos for tract,wedge,wedgePop,r= 204 1 603201.2949209217 3.6503\n",
      "we have 2 non-opposing shorted wedges for tract no 208\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 209 3.0 1 90.0 0.6197 274985.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 209 4.0 1 90.0 0.5144 274985.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 209 5.0 1 90.0 0.498 274985.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 209 6.0 1 90.0 0.482 274985.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 209 7.0 1 90.0 0.4666 274985.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 209 8.0 1 90.0 0.4517 274985.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 209 9.0 1 90.0 0.4372 274985.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 209 10.0 1 90.0 0.4232 274985.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 209 11.0 1 90.0 0.4097 274985.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 209 12.0 1 90.0 0.3966 274985.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 209 13.0 1 90.0 0.3839 274985.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 209 14.0 1 90.0 0.3716 274985.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 209 15.0 1 90.0 0.3597 274985.2\n",
      "I am working on tract number 220 of 1784 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 225\n",
      "I am working on tract number 240 of 1784 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 256\n",
      "we have 2 non-opposing shorted wedges for tract no 258\n",
      "I am working on tract number 260 of 1784 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 264 5.0 0 90.0 0.6287 239988.5\n",
      "I am working on tract number 280 of 1784 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 288 3.0 1 90.0 1.7679 165936.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 295 3.0 0 90.0 1.3966 210792.8\n",
      "I am working on tract number 300 of 1784 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 312 3.0 2 90.0 1.9727 200516.7\n",
      "I am working on tract number 320 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 336 2 218403.07284419733 2.2901\n",
      "8 yoyos for tract,wedge,wedgePop,r= 336 2 29501.485408042907 1.1451\n",
      "9 yoyos for tract,wedge,wedgePop,r= 336 2 514740.29326823796 3.352\n",
      "10 yoyos for tract,wedge,wedgePop,r= 336 2 182099.9647482658 2.2485\n",
      "11 yoyos for tract,wedge,wedgePop,r= 336 2 429189.4179288075 2.8003\n",
      "11 yoyos for tract,wedge,wedgePop,r= 336 2 384708.614924277 2.5244\n",
      "11 yoyos for tract,wedge,wedgePop,r= 336 2 285424.7174375488 2.3865\n",
      "11 yoyos for tract,wedge,wedgePop,r= 336 2 237673.8101681269 2.3175\n",
      "I am working on tract number 340 of 1784 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 341 2.0 1 90.0 1.0424 359397.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 341 5.0 0 90.0 1.1811 258753.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 341 6.0 0 90.0 0.9381 258753.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 354 18.0 1 85.6 3.8655 755379.2\n",
      "loop31.0, tr354,wedgePops770152.03, 23936.1, 117523.9, 24096.4, 604595.6, Overedge?1, 1, 0, 0, ,Satisfied?0010,yoyo?0006 \n",
      "   targetWP, latest drx4 are tWP,dr, 604953.3,1.2664, 604953.3,-1.7886, 24113.4,0.0002, 604953.3,0.0053\n",
      "I am working on tract number 360 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 363 2 358163.542334063 1.7343\n",
      "8 yoyos for tract,wedge,wedgePop,r= 363 2 18121.399156748026 0.8671\n",
      "9 yoyos for tract,wedge,wedgePop,r= 363 2 564811.1682108783 2.5643\n",
      "9 yoyos for tract,wedge,wedgePop,r= 363 2 329391.2713841889 1.7157\n",
      "10 yoyos for tract,wedge,wedgePop,r= 363 2 24514.21487023332 1.2914\n",
      "10 yoyos for tract,wedge,wedgePop,r= 363 2 51199.30008671887 1.5036\n",
      "10 yoyos for tract,wedge,wedgePop,r= 363 2 144945.3424705104 1.6097\n",
      "11 yoyos for tract,wedge,wedgePop,r= 363 2 216068.86053132714 1.6627\n",
      "12 yoyos for tract,wedge,wedgePop,r= 363 2 177763.97984109656 1.6362\n",
      "7 yoyos for tract,wedge,wedgePop,r= 369 3 438804.96575611015 3.3457\n",
      "8 yoyos for tract,wedge,wedgePop,r= 369 3 36096.399641577445 1.6728\n",
      "9 yoyos for tract,wedge,wedgePop,r= 369 3 438786.2885424557 3.3456\n",
      "9 yoyos for tract,wedge,wedgePop,r= 369 3 233788.77013523548 2.5092\n",
      "10 yoyos for tract,wedge,wedgePop,r= 369 3 60823.264081875386 2.091\n",
      "10 yoyos for tract,wedge,wedgePop,r= 369 3 90634.92420744427 2.3001\n",
      "10 yoyos for tract,wedge,wedgePop,r= 369 3 140430.48637957667 2.4047\n",
      "10 yoyos for tract,wedge,wedgePop,r= 369 3 184740.76394012498 2.4569\n",
      "11 yoyos for tract,wedge,wedgePop,r= 369 3 209190.3103573623 2.4831\n",
      "11 yoyos for tract,wedge,wedgePop,r= 369 3 196451.3290818968 2.47\n",
      "7 yoyos for tract,wedge,wedgePop,r= 371 2 231109.50179610756 1.4538\n",
      "8 yoyos for tract,wedge,wedgePop,r= 371 2 121408.62466922252 0.7269\n",
      "9 yoyos for tract,wedge,wedgePop,r= 371 2 231515.02442033673 1.459\n",
      "10 yoyos for tract,wedge,wedgePop,r= 371 2 146824.98723561273 1.093\n",
      "11 yoyos for tract,wedge,wedgePop,r= 371 2 198657.94239498375 1.276\n",
      "12 yoyos for tract,wedge,wedgePop,r= 371 2 154759.05904902378 1.1845\n",
      "12 yoyos for tract,wedge,wedgePop,r= 371 2 173076.09988726152 1.2302\n",
      "12 yoyos for tract,wedge,wedgePop,r= 371 2 184376.53242910173 1.2531\n",
      "I am working on tract number 380 of 1784 tracts\n",
      "I am working on tract number 400 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 402 3 487809.8921194832 3.1497\n",
      "8 yoyos for tract,wedge,wedgePop,r= 402 3 53220.07810270827 1.5748\n",
      "9 yoyos for tract,wedge,wedgePop,r= 402 3 487637.98843744193 3.1485\n",
      "9 yoyos for tract,wedge,wedgePop,r= 402 3 315343.7499875793 2.3617\n",
      "10 yoyos for tract,wedge,wedgePop,r= 402 3 101299.97658184095 1.9683\n",
      "11 yoyos for tract,wedge,wedgePop,r= 402 3 247379.30458549532 2.165\n",
      "12 yoyos for tract,wedge,wedgePop,r= 402 3 173088.6553732344 2.0666\n",
      "13 yoyos for tract,wedge,wedgePop,r= 402 3 223651.14291015145 2.1158\n",
      "13 yoyos for tract,wedge,wedgePop,r= 402 3 202429.82421084057 2.0912\n",
      "loop31.0, tr402,wedgePops766422.45, 192667.9, 192441.9, 192319.0, 188993.7, Overedge?0, 0, 0, 0, ,Satisfied?1110,yoyo?12213 \n",
      "   targetWP, latest drx4 are tWP,dr, 192631.6,-0.0025, 192631.6,0.0003, 192631.6,0.0004, 192631.6,-0.0123\n",
      "14 yoyos for tract,wedge,wedgePop,r= 402 3 188993.6556473035 2.0789\n",
      "loop32.0, tr402,wedgePops773521.37, 192667.9, 192441.9, 192319.0, 196092.6, Overedge?0, 0, 0, 0, ,Satisfied?1110,yoyo?12214 \n",
      "   targetWP, latest drx4 are tWP,dr, 192631.6,-0.0025, 192631.6,0.0003, 192631.6,0.0004, 192631.6,0.0061\n",
      "7 yoyos for tract,wedge,wedgePop,r= 408 2 231231.7517041687 2.4801\n",
      "8 yoyos for tract,wedge,wedgePop,r= 408 2 105795.62816678057 1.2401\n",
      "9 yoyos for tract,wedge,wedgePop,r= 408 2 231299.85306818344 2.4812\n",
      "10 yoyos for tract,wedge,wedgePop,r= 408 2 127148.1221435136 1.8606\n",
      "10 yoyos for tract,wedge,wedgePop,r= 408 2 152729.81075027687 2.1709\n",
      "11 yoyos for tract,wedge,wedgePop,r= 408 2 219517.97493624093 2.326\n",
      "12 yoyos for tract,wedge,wedgePop,r= 408 2 178307.0074152264 2.2485\n",
      "13 yoyos for tract,wedge,wedgePop,r= 408 2 201123.2536720729 2.2873\n",
      "we have 2 non-opposing shorted wedges for tract no 415\n",
      "we have 2 non-opposing shorted wedges for tract no 416\n",
      "7 yoyos for tract,wedge,wedgePop,r= 416 2 291614.3172313076 2.8895\n",
      "8 yoyos for tract,wedge,wedgePop,r= 416 2 151763.9345938518 1.4448\n",
      "9 yoyos for tract,wedge,wedgePop,r= 416 2 283326.4264681318 2.8496\n",
      "10 yoyos for tract,wedge,wedgePop,r= 416 2 176504.2429962039 2.1472\n",
      "10 yoyos for tract,wedge,wedgePop,r= 416 2 210737.63993632645 2.4984\n",
      "11 yoyos for tract,wedge,wedgePop,r= 416 2 260784.03091207717 2.674\n",
      "12 yoyos for tract,wedge,wedgePop,r= 416 2 220140.12170027525 2.5862\n",
      "12 yoyos for tract,wedge,wedgePop,r= 416 2 238796.2325055986 2.6301\n",
      "13 yoyos for tract,wedge,wedgePop,r= 416 2 251135.12823963806 2.652\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_391/1310157012.py:66: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
      "/tmp/ipykernel_391/1310157012.py:99: RuntimeWarning: invalid value encountered in multiply\n",
      "  wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 420 of 1784 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 431\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 437 4.0 0 90.0 1.4425 203836.2\n",
      "I am working on tract number 440 of 1784 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 446 5.0 1 90.0 2.8882 324348.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 446 1 523309.9449264081 2.4984\n",
      "8 yoyos for tract,wedge,wedgePop,r= 446 1 143211.97790474677 1.2492\n",
      "9 yoyos for tract,wedge,wedgePop,r= 446 1 520533.4955551131 2.4084\n",
      "10 yoyos for tract,wedge,wedgePop,r= 446 1 303345.90383976075 1.8288\n",
      "11 yoyos for tract,wedge,wedgePop,r= 446 1 502652.521929399 2.1186\n",
      "11 yoyos for tract,wedge,wedgePop,r= 446 1 414159.98009937885 1.9737\n",
      "11 yoyos for tract,wedge,wedgePop,r= 446 1 354641.0814514621 1.9013\n",
      "12 yoyos for tract,wedge,wedgePop,r= 446 1 325489.1185462657 1.865\n",
      "loop31.0, tr446,wedgePops764167.68, 38219.3, 340196.3, 38168.6, 347583.5, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01201 \n",
      "   targetWP, latest drx4 are tWP,dr, 347044.0,1.44, 347044.0,0.0181, 38219.3,0.0008, 347044.0,-0.0014\n",
      "12 yoyos for tract,wedge,wedgePop,r= 446 1 340196.25729656627 1.8831\n",
      "loop32.0, tr446,wedgePops771335.73, 38219.3, 347364.3, 38168.6, 347583.5, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01201 \n",
      "   targetWP, latest drx4 are tWP,dr, 347044.0,1.44, 347044.0,0.0091, 38219.3,0.0008, 347044.0,-0.0014\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 448 4.0 0 120.8 1.6934 412741.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 448 5.0 0 120.8 1.4942 412741.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 448 6.0 0 120.8 1.3183 412741.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 448 7.0 0 120.8 1.1632 412741.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 448 8.0 0 120.8 1.0263 412741.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 448 13.0 0 120.8 0.9715 412741.6\n",
      "we have 2 OPPOSING shorted wedges for tract no 453\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 455 6.0 3 90.0 2.5787 361669.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 455 11.0 3 90.0 2.6192 361669.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 455 16.0 3 90.0 2.9593 361669.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 455 3 361420.007095958 2.1621\n",
      "8 yoyos for tract,wedge,wedgePop,r= 455 3 8133.156598120695 1.081\n",
      "9 yoyos for tract,wedge,wedgePop,r= 455 3 361669.8687862514 2.7814\n",
      "9 yoyos for tract,wedge,wedgePop,r= 455 3 355437.0038010206 1.9312\n",
      "10 yoyos for tract,wedge,wedgePop,r= 455 3 25317.32482571964 1.5061\n",
      "10 yoyos for tract,wedge,wedgePop,r= 455 3 190511.53867483768 1.7187\n",
      "11 yoyos for tract,wedge,wedgePop,r= 455 3 294362.95271289244 1.825\n",
      "11 yoyos for tract,wedge,wedgePop,r= 455 3 269878.1995473869 1.7718\n",
      "12 yoyos for tract,wedge,wedgePop,r= 455 3 231764.49031758728 1.7453\n",
      "13 yoyos for tract,wedge,wedgePop,r= 455 3 255204.40869103587 1.7585\n",
      "13 yoyos for tract,wedge,wedgePop,r= 455 3 243999.75386578424 1.7519\n",
      "loop31.0, tr455,wedgePops771553.72, 235797.9, 61895.2, 235869.4, 237991.2, Overedge?0, 1, 0, 0, ,Satisfied?1010,yoyo?30013 \n",
      "   targetWP, latest drx4 are tWP,dr, 236210.5,0.0008, 236210.5,1.44, 236210.5,0.0107, 236210.5,-0.0033\n",
      "we have 2 non-opposing shorted wedges for tract no 457\n",
      "I am working on tract number 460 of 1784 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 462\n",
      "I am working on tract number 480 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 486 0 605929.3498606588 4.0881\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 487 9.0 1 36.0 4.9356 401977.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 487 15.0 1 36.0 5.1622 401977.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 487 16.0 1 36.0 4.9885 401977.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 487 17.0 1 36.0 4.8206 401977.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 487 23.0 1 36.0 6.0529 401977.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 487 24.0 1 36.0 5.8493 401977.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 487 25.0 1 36.0 5.6524 401977.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 487 26.0 1 36.0 5.4622 401977.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 487 27.0 1 36.0 5.2784 401977.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 487 28.0 1 36.0 5.1008 401977.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 487 29.0 1 36.0 4.9292 401977.4\n",
      "loop31.0, tr487,wedgePops517007.91, 1074.9, 130990.3, 1161.6, 383781.2, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0504 \n",
      "   targetWP, latest drx4 are tWP,dr, 384144.3,0.3957, 384144.3,-2.3816, 1163.1,0.002, 384144.3,0.0009\n",
      "loop32.0, tr487,wedgePops728203.05, 1074.9, 342185.4, 1161.6, 383781.2, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0604 \n",
      "   targetWP, latest drx4 are tWP,dr, 384144.3,0.3957, 384144.3,1.8103, 1163.1,0.002, 384144.3,0.0009\n",
      "loop33.0, tr487,wedgePops738972.85, 1074.9, 352955.2, 1161.6, 383781.2, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0604 \n",
      "   targetWP, latest drx4 are tWP,dr, 384144.3,0.3957, 384144.3,0.2185, 1163.1,0.002, 384144.3,0.0009\n",
      "loop34.0, tr487,wedgePops787995.09, 1074.9, 401977.4, 1161.6, 383781.2, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0604 \n",
      "   targetWP, latest drx4 are tWP,dr, 384144.3,0.3957, 384144.3,0.4632, 1163.1,0.002, 384144.3,0.0009\n",
      "7 yoyos for tract,wedge,wedgePop,r= 487 1 401977.4414592744 4.8736\n",
      "loop35.0, tr487,wedgePops572201.74, 1074.9, 186184.1, 1161.6, 383781.2, Overedge?1, 1, 0, 0, ,Satisfied?0011,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 582104.5,0.3957, 582104.5,-2.4368, 1163.1,0.002, 582104.5,0.0009\n",
      "loop36.0, tr487,wedgePops689295.77, 1074.9, 186184.1, 1161.6, 500875.2, Overedge?1, 1, 0, 0, ,Satisfied?0010,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 582104.5,0.3957, 582104.5,-2.4368, 1163.1,0.002, 582104.5,0.0827\n",
      "loop37.0, tr487,wedgePops707876.26, 1074.9, 186184.1, 1161.6, 519455.7, Overedge?1, 1, 0, 0, ,Satisfied?0010,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 582104.5,0.3957, 582104.5,-2.4368, 1163.1,0.002, 582104.5,0.0435\n",
      "loop38.0, tr487,wedgePops725620.35, 1074.9, 186184.1, 1161.6, 537199.8, Overedge?1, 1, 0, 0, ,Satisfied?0010,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 582104.5,0.3957, 582104.5,-2.4368, 1163.1,0.002, 582104.5,0.1096\n",
      "loop39.0, tr487,wedgePops795903.74, 1074.9, 186184.1, 1161.6, 607483.2, Overedge?1, 1, 0, 0, ,Satisfied?0010,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 582104.5,0.3957, 582104.5,-2.4368, 1163.1,0.002, 582104.5,0.1965\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 0.3983 7.2082 1074.9098 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 2.43682 401977.4415 186184.0855 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.06285 1155.6347 1161.586 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 1.62274 537199.7721 607483.1542 0\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAArBElEQVR4nO3dd3hUZfbA8e9JAoQWOihERF3dFf1ZEUUQqSoJC1JUUIoCIgoLFkQR3VXXirrgig0REbGghCYE0FBUBFdAsQAWRFRABYHQnDtp5/fHTCCEhEySaXc4n+fJk5k79517rpEzZ9773vcVVcUYY0zsiot0AMYYY0LLEr0xxsQ4S/TGGBPjLNEbY0yMs0RvjDExLiHSARSlbt262qRJk0iHYYwxrrFmzZo/VLVeUa9FZaJv0qQJq1evjnQYxhjjGiLyU3GvWdeNMcbEOEv0xhgT4yzRG2NMjLNEb4wxMc4SvTHGxLiAE72IxIvI5yIyz/98uois9f9sFpG1xbS7QkS+FZGNInJ3kOI2xhgToNIMrxwBbACSAFT1mvwXROQpYE/hBiISDzwLdAS2AKtEZK6qri9P0MYYYwIXUEUvIslAKjCpiNcEuBp4s4imzYGNqrpJVbOAt4CuZQ/XGGNijzcriwVLl7NsZWjuHwq0oh8PjAKqF/HaJcDvqvp9Ea81An4p8HwLcGFRBxCRwcBggMaNGwcYljHGuJOqsuarDaSlZzD3vQ/I3LuPRsfV59N504J+rBITvYh0Brar6hoRaVPELr0pupoHkCK2FbnSiapOBCYCNGvWzFZDMcbEpJ+3/kragsXMmJ/B5l+2kVipEle0uZjNv2wjc+++kBwzkIq+JdBFRFKARCBJRKapah8RSQC6A+cX03YLcEKB58nAtvIEbIwxbrNn337mZXxIWnoG//v8awBanH82w2/oTUq7VlSvVpVBdz6A4/WG5PglJnpVHQ2MBvBX9CNVtY//5Q7AN6q6pZjmq4BTReQkYCvQC7i2nDEbY0zUy87JYdnK1aSlL+a9D1bgzcrmlBOTueuWG+jeqR3Jxzc4bH/H66VyYqWQxFLeSc16UajbRkQaApNUNUVVc0RkGLAIiAcmq+q6ch7TGGOikqry1TffMyN9MbMXLmHn7j3UqpHEtVem0DO1A2c3PQ3f+JUjeRwviYmJIYmrVIleVZcBywo8v76IfbYBKQWepwPpZQ3QGGOi3dbftjNr4RLS0hfz3aafqFihAh0uuZCrUjvS5uJmVKxQocT3cJwsatdMCkl8UTlNsTHGRLv9B/4kfely0tIX8/GqtagqF5x9Bo+NHs7fO15KzaSiBikWz+M4JCYWOZ18uVmiN8aYAOXm5vLxqrW8Mz+DBUuX43G8NG50HLcNuo7uKe056YRGZX5vx5tFYqXo7KM3xpiYt2Hjj6TNz2DWwiX8tmMnSdWq0r1Te65K7Uizs5sW2+9eGh4nei/GGmNMTNr+xy5mL1rKjPkZrPvuBxLi42l78QU8cMfNdLjkIhIrVQzq8XxdN5bojTEmpDyOl/c+WMGM+Rl88L815ObmcXbT0/j3yFvoenkb6tSqGbJjO94sq+iNMSYU8vLy+OSzr0hLz2De4o/Yf+BPGjaox839rqZnSgdOPSn0U7Lk5OSSnZMT9G8J+SzRG2OOSRs3/0JaegZp6YvZ+tt2qlapTGr7S+iZ2oEW551FXFz4luvIvyO2cjSMozfGGDfblbmHue99wIz57/P5um+Ji4uj9YXnMXrYAK5oc3HIEm1JPI4v0VtFb4wxZeDNyiLjo/+Rlp7B4uWfkpOby+mnnsx9tw6m2xVtaVC3TqRDtIreGGNKS1VZ/eV60tIX8+77vimA69epzcDe3eiR0p4zTjsl0iEexip6Y4wJ0E9bfmVm/hTAW3xTAHdqezE9UzvS6oJzSUiIj3SIRTpU0duoG2OMOcKefft59/0PSEtfzKdrv0ZEaHH+WQwfeC0pbVtSvVrVSIdYokMVvSV6Y4wBfFMAL12xirT0xbz/4Uq8Wdn8pckJ3D30Brp3ak+j4+pHOsRSsYreGGPw9bt/ueF70tIzmL1oKTt376F2zRpc1y2FnqkdOev0U4MyFUEk5Ff0la2iN8Yci/KnAJ4xP4Pvf/yZihUq0LH1RfRM7UDbiy+gQoL705jjZAHYFAjGmGNH/hTAM+ZnsGL1FwenAH78nhF07tC61FMARzuP4wDWdWOMiXG5ubl89OnnpKVnkL7kYxyvlxMbHc/tN/ahe0p7miQ3jHSIIeN4fRW9JXpjTExa//0m0tIzmLVgCb//sYsa1avRM7UDPVM70Oys4EwBHO1sHL0xJuZs/2OXr989PYP1320iIT6edi2b0zO1A+1bXRiyhBet8kfd2PBKY4yreRyHhctWkJaewQeffEZeXh7nNP0rD905lC6XXRrSKYCjncfxUqlihZBNpGaJ3hgTMnl5eaz87EvS5i9m/pJDUwAP7X8NPVPb85cmoZ8C2A0crzekE6pZojfGBN3GzT8zY/5iZi44NAVw5/at6ZnagYvO+7+wTgHsBh7HG9LuKkv0xpig2Lk7kzmLlpGWvpi1631TAF960XncM2wgl7dpEbEpgN3A43hDNoYeLNEbY8rB8WaR8dEnpKUvZsnHvimAm552Mv+8dTBXRskUwG4QNV03IhIPrAa2qmpn/7Z/AMOAHGC+qo4qot1mYB+QC+SoarMgxG2MiRBVZfUX65mRnsG773/Ann37aVC3NoOu7UaPlA40PfXkSIfoOtHUdTMC2AAkAYhIW6ArcJaqekXkaLMItVXVP8oepjEm0jZv2cbM9MWkpS8+OAVwSruW9EztQKsLziU+PjqnAHYDX0Uf4a4bEUkGUoGHgdv9m28GHlNVL4Cqbg9JhMaYiMncu495GR8yY34Gq75Yh4hwcbOzGTHoWlLatqJa1SqRDjEmeBwvdWvXCtn7B1rRjwdGAQUnmDgNuEREHgYcYKSqriqirQLviYgCL6rqxKIOICKDgcEAjRvbkCtjIiV/CuAZ8zN4/8NPyMrO5tSTGjN62AC6XdHOdVMAu4HjzYpsRS8inYHtqrpGRNoUalsLuAi4AHhbRE5WVS30Fi1VdZu/a+d9EflGVT8sfBz/B8BEgGbNmhV+D2NMCKkqX6z/zj8F8DJ2ZfqmAO7bI5WeqR34v7+5dwpgN/A4TsjuioXAKvqWQBcRSQESgSQRmQZsAWb6E/unIpIH1AV2FGysqtv8v7eLyCygOXBEojfGhN/W37YfXHpv4+ZfqFSxAh1bt6BnagfatGgWE1MAu0HEK3pVHQ2MBvBX9CNVtY+IDAHaActE5DSgInDYBVcRqQrEqeo+/+PLgAeDegbGmFLZt/8A6UuWMyM9g5VrvkRVaX7OmYwd04POHVpTo3q1SId4zImmUTeFTQYmi8jXQBbQX1VVRBoCk1Q1BWgAzPJ/5UsA3lDVheUN2hhTOjk5uXz06WekpWewYOkKHK+XJskNuWNwX7p3as+JycdHOsRjmuNEyTh6AFVdBizzP84C+hSxzzYgxf94E3B2eYM0xpTNuu9+IC19MbMWLGH7Tt8UwFd17kjP1A6c/3+nW797FMjOySEnNzdqK3pjTBT6bcdOZi9cwoz0xWz43jcFcPtWzemZ2pH2rZpTqeKxNQVwtHOc0C4MDpbojYkJf3o8/imAF/Ph/3xTAJ97xl95eNQwulx2KbVr1oh0iKYYBxcdsURvjCnOS2/M5IkXXuXAnx4aHVefYddfQ4+UDvylyQmRDs0EIH/REavojTHFmrNoKfXr1uaJMbdx4bln2hTALnNoGUFL9MaYYjjeLP568om0OP+sSIdiyiDUC4MD2Ee/MS7ncUI7IZYJLY/jAKGt6GMy0c9ZtIMWXVfz4rSteJzcSIdjTEg5Xm9Ik4QJLavoy+irbw7w81YvD47fTKtun/HqjF/Jys6LdFjGhIRV9O5mFX0ZeZxcatZI4J0XzuSEhpW457FNtO7xGdPn/k5Ojs2XZmJLqFcnMqFlFX0ZOd48KifGcXGzGsya9H+8/kxTateswO0PbqTt1Z8xZ9EO8vIs4Rv3y83NxZuVHdK7Kk1oHRp1E7q/YUwmeo+TR2Il36mJCG1a1GL+q2fx8pN/o2KFOG4Z8x0nNF/B4Lu+4chZlY1xD29W6KtBE1r5XTeh/FYWk4neV9EfvqyZiHBFmzq8/+Y5PPfwaQDMX7wTb5YleuNe4RiDbULLcfI/rK2iL5WCFX1hcXFC18vr0bmDb3X64vYzxg3C0b9rQsvjvzM2lHMQxWSWy++jP5rsbOX0U229S+Nu4RixYULLcXzDY0N5R3NMJnqPk1tipe54i6/6jXGLQ1/7LdG7lcfxkhjCbhuI0UQfSEXvcY7sxzfGbQ7NfGijbtzKNzw2tB/UMZnoj9ZHn8/xllz1GxPtwjFiw4SWxwn9nc0xmemKGnVTmK+ij8nTN8eQ/IuxNo7evayiLyProzfHCqvo3c8q+jJQ1VL00cfc6ZtjjFX07ud4s6yiL63sHCU3t+Tx8VbRm1jgCcN6oya0PI5jFX1peRzfLJUlV/S5NurGuF7+MnQ2jt69rKIvA8ebn+iLT+LZOXkBVf3GRDubAsH9fH30No6+VPIXGjlaEg+06jcm2jleLxUSEkhIsG+nbhWOaaYDznQiEi8in4vIvALb/iEi34rIOhEZW0y7K/z7bBSRu4MR9NE4ASTx/H0SLdEbl7NFR9wvHBV9aRYHHwFsAJIARKQt0BU4S1W9IlK/cAMRiQeeBToCW4BVIjJXVdeXO/JieAJI4ocqequCjLvZMoLu5hslGCXj6EUkGUgFJhXYfDPwmKp6AVR1exFNmwMbVXWTqmYBb+H7cAiZQ330R6no/ftYH71xO6vo3S07J4fc3LyoGXUzHhgFFFx49TTgEhH5n4h8ICIXFNGuEfBLgedb/NuOICKDRWS1iKzesWNHgGEd6WBFX6n4aj2/H9/66I3bOU6WjaF3sXBNM11iphORzsB2VV1T6KUEoBZwEXAn8LaISOHmRbxlkSt9qOpEVW2mqs3q1atXcuTFsIreHEs8XsfuinWxcE0zHUgffUugi4ikAIlAkohMw1edz1TfWnyfikgeUBcoWI5vAU4o8DwZ2BaUyItho27MscRxsmzmShcL1w1vJWY6VR2tqsmq2gToBSxR1T7AbKAdgIicBlQE/ijUfBVwqoicJCIV/e3nBi/8I1lFb44l1kfvbs7Baaajo4++KJOBk0Xka3wXWfurqopIQxFJB1DVHGAYsAjfiJ23VXVdeYM+mkN99Eer6PP76G3UjXE3G3XjbuGq6EszvBJVXQYs8z/OAvoUsc82IKXA83QgvTxBlkYgd8YGUvUb4wZW0btbuCali7lM53FyEYFKFYu6Dpy/j3XdmNhgFb27hWua6ZjLdPmzUh45AKjAPnYx1sQIq+jdzSr6MgpkGUGPXYw1McLxZllF72JW0ZeRE8CCIvkrUB2t6jcm2uXm5pKVnW0VvYtZRV9GAS0M7uTZhGbG9cJ1V6UJnagZR+82tjC4OVaE665KEzr5C8dY100peZzcEqt1W0bQxAKr6N3P43gRESpVrBDS48RctgukWvftYzdLGXc7tLqUTYHgVr7hsRVDfr0w5hJ9INW6VfQmFoTra78JHd+iI6H/RlaqO2PdILCKPveY7aP/Yv2vPPHCB1SqmEDjRjU5MbkWTZJrcdIJtWjcqKaNRHIRq+jdz3FCvzA4xGCi91XrR++Wcbx5JFWPuVMPyOTpq1m6YlORr511+nEseG1AmCMyZRWuMdgmdDyOE5aKPubK2kCq9WN51M2T96bw3MMtaX3hTuB9YDVnN60FwN1D20QyNFNKjuMfg23TFLuW47WKvkysj75ov27fyqwFbzJj/jS+3XT4kr1frJ9H+1Yp1KnZKULRmbII1xhsEzoexwn5FMUQYxW9qtqomwIO/LmfGfOn0XtoCheknszDz9xDtWpJPHr3BL5e/BtbV2fx3Ye7GD3sIdZ8uZLL+1zI4Lt68d2mkK3dboIo/2KsjaN3L6voyyArW1EteQ4bx5sbsxV9bm4uH69exoz501iwdDZ/eg7QuNFJ3DroHrp3upaTG5962P5Vq1Rj2PWj6NtjMC+98TQTX3+a9CWz6HZFb+646T6aJJ8SoTMxJbGK3v08jpcaSdVCfpyYSvSHlggsvloPtOp3m29/WMeM+dOYufAtftu+laRqNeh2RS96pvbhgrMvLnE0TY3qNRl5078YcM1Qnp/6FJOnP8ec96bTq8v1jBg4mkbHNQ7TmZhAWUXvfh4nPGv+xlSid7wlrxebnaPk5cXGzJU7dv7O7EXTSUt/na+++Zz4+HjaXnw599/+BB0v6UxipdL/D1S7Zl3GDH+UQdcOZ8IrY5k28yXemfcafbrfyD9uuIv6dY8LwZmYsrDhle7nm3009H+/mEr0gSz67faFwbOys1iwdDZp6W+wbOUicnNzOev083hw5H/oetnV1K1dPyjHaVD3eP595ziG9L2Np19+lFdnvMAbsycz4JpbuLnfSGrXrBOU45iyc7xZVKxQgfj42L/eFKvCtZ5ATCX6QBb9dvvC4BNfH8+jE+49+DwuLo7de3bx9rtTWfTBu9RMqnXwp0b1WtSsUZsa1WtSs0ZtalavSY2kWtRMqk21qtUDujmq0XGNGTvmeW7udwf/mfgQz7/2H6amTeTGa4cz+LpbSapWI5Sna47C97Xfum3cLFwrhMVUog+sonf3wuB9ut9IwwbJZO7ZTebeXWTuy2TPXt/jPXsz+W7TevbszSRz7y6ysrOKfZ/4+HhqVK9FjaSa1EyqfdiHQ/62Gkm1qJVUy//hUIsxwx/hxmuHM2HKWMa99DCvTH+OIX1vZ2CvYVSpXDWM/xUMhO9rvwkN3/VCq+hLLX+JwKPNXhnIPtGsZlItune6tsT9VBXH62H3nl3s2bebzD272bNvN3v2ZrJ77y7/h8Pug793Z+7kx583krnPt01Vi33vxEqJxMfHk7l3N489ex+T3nyGZx6cQuuLOgTzVE0JwnUhz4RGVnY2qmoVfWkFUq27vY8+UCJC5cQqVE6sQsMGyaVqm5eXx74De8ncs4s9+zLJLPCNIbPQh8TuvbvZuy+TXZl/hOhMTHGsone3cA6PjalEX5o++lhP9OURFxdHjeo1qVG9ZqRDMUdhC4O7W7iWEYQYuzO2NKNuSpr4zJhoF64LeSY0wjkpXcCJXkTiReRzEZnnf36/iGwVkbX+n5Ri2m0Wka/8+6wOVuBFsYreHEusone3cFb0pem6GQFsAJIKbBunqk8G0Latqoa8EzeQO2Pz+/HdOrzSmHxOmBatMKERdRW9iCQDqcCk0IZTPoEkcavoTazweL1hmfnQhEY4p5kONNuNB0YBeYW2DxORL0VksojUKqatAu+JyBoRGVzcAURksIisFpHVO3bsCDCswznePESgUsXibwQ61Edvid64m291Iht141bhHHVTYrYTkc7AdlVdU+il54FTgHOAX4GninmLlqp6HtAJGCoirYvaSVUnqmozVW1Wr169QOM/jMfxzTN/tDs+D1X0djHWuJtvLnMbR+9W4VzzN5CytiXQRUQ2A28B7URkmqr+rqq5qpoHvAQ0L6qxqm7z/94OzCpuv2BwvIGtF1tS1W+MGzjeLCrbOHrXCuekdCUmelUdrarJqtoE6AUsUdU+InJ8gd26AV8XbisiVUWkev5j4LKi9guW/Io+kH1sEWzjZjk5uWTn5FgfvYuFs6Ivzw1TY0XkHHx98JuBmwBEpCEwSVVTgAbALH9STQDeUNWF5Qn4aBxvboldMsfiMoIm9hxMEjbqxrXCWdGXKtGr6jJgmf9x32L22Qak+B9vAs4uV4SlEGhFbyNujNsdTBJW0bvWoYo+Ci7GukkgffROAB8GxkS7cH7tN6HhcbyICBUrVAj5sWIq43mcvBJnpfQ4JXfvGBPtbHUp98tfGDwc1wtjKtE7Th6Vj1Kt78rM5tftWa6dotiYfLYwuPt5HCdsdzbH1OyVHif3iCTuzcpj8fLdpKVvZ/Hy3WTnKNdfZeueGnezhcHdL5xzFcVUovf10cejqqz5ah8z5u/g3ff/IHNvDvXrVGBAr+PpkVKfM06z1ZCMu1lF737hXE8gphK9x8njs6/20ar7Z2z+xSGxUhyd2tamZ2p9Wl1Qk4QEGztvYoMNr3S/cK4QFlOJvkKC8MNPHlqcX4PhA5JJaVuH6tVi6hSNAWx4ZSywir6M3nnxTConxtPoOPuf38Q2x7puXM/jeKliffSl95cmVSIdgjFhYX307uc4XmrXSCp5xyCwcYbGuFA4VycyoRHOUTeW6I1xofzViWx4pXs5YVw4xhK9MS7keLOoVLECcXH2T9itrKI3xhyVL0nYPDdu5njDt+avJXpjXMiXJKx/3q1U1Sp6Y8zReRxbGNzNvFnZQPgupluiN8aFHK/X7op1sfyL6eHqfrNEb4wLWUXvbuEeHmuJ3hgXCueFPBN84b7hzRK9MS4Uzgt5JvjCPc20JXpjXCicE2KZ4LOK3hhTonBOcWuCL9xr/lqiN8aFrKJ3t3Cv+WuJ3hgXsj56d7OuG2NMiRwbXulqTpgXjgk40YtIvIh8LiLz/M/vF5GtIrLW/5NSTLsrRORbEdkoIncHK3BjjlXZOTnk5ObaDVMuFu6KvjQLj4wANgAFZ8ofp6pPFtdAROKBZ4GOwBZglYjMVdX1ZQnWGBP+atAEX1QOrxSRZCAVmFTK928ObFTVTaqaBbwFdC3lexhjCrDVpdwvWvvoxwOjgLxC24eJyJciMllEahXRrhHwS4HnW/zbjiAig0VktYis3rFjR4BhGXPsOTRiwxK9WzleL3FxcVRICM9qriUmehHpDGxX1TWFXnoeOAU4B/gVeKqo5kVs06KOo6oTVbWZqjarV69eSWEZc8w6NAbbEr1b5Y+aEikqRQZfIB8nLYEu/outiUCSiExT1T75O4jIS8C8ItpuAU4o8DwZ2FaOeI055lnXjfuF+z6IEit6VR2tqsmq2gToBSxR1T4icnyB3boBXxfRfBVwqoicJCIV/e3nBiFuY45Z4b6QZ4Iv3Hc2l6eDaKyInIOvK2YzcBOAiDQEJqlqiqrmiMgwYBEQD0xW1XXlC9mYY5tV9O4X7oq+VIleVZcBy/yP+xazzzYgpcDzdCC9zBEaYw5jFb37hfvOZrsz1hiXsYre/cK9noAlemNc5sCfHiB8E2KZ4LOK3hhzVNPnLgJg1sIlZOfkRDgaUxZW0Rtjjqp9qwsBeHTCZNr0HERaega5ubkRjsqUhlX0xpijuu3GPmxZtYgp4x6kSpVEhv9zLO173cS8jA/Jyyt887qJRlbRG2NKJCJ0vOQiFk17jhcfuxeAm+5+iE59h5Gx/H+oFnkDuokSVtEbYwIWFxdH5w6tWfzWizz9wCj27T9A/1vvo8uAW/no088jHZ4phuNkkZgYRXfGGmOiX3x8PD1TO/BB2suMHXMrv27fQa9b7uKqIXeyaq3doxhN8vLycLzesN4Za4nemBhSISGB67qlsHzmFB4ceTPf//gzVw66jb7Dx/Dlhu8iHZ4BvFlZQHiHx1qiNyYGJVaqyMBe3Vgxewpj/jGIz9Z9Q6e+wxh05wN8s/HHSId3TPM4vkRvffTGmKCoUrkyt/S/mk/mTOWOwX1Z/unndOg9hGH3Psqmn7dGOrxjksdxAKzrxhgTXNWrVeX2wX1ZMWcqQ/tfzcJlK2hz1UDuePAptvz6e6TDO6Y4Xuu6McaEUO2aSYweNpCVc6Zy/dVdmblgCa263cCYxyfw246dkQ7vmBCJuYos0RtzDKpXpxYP3nEzy2e9wjVdLmfazPm0vLI/D46fyM7dmZEOL6ZFYvZRS/TGHMMaHVefx+8ZwYdpk+ncoTUvvTGTFl37M/b5KezZtz/S4cUkq+iNMRFxYvLxPP3AKJZMn0i7ls15+uU3aNGlH0+//Ab7D/wZ6fBiilX0xpiIOvWkxrzw6Bjee+N5mp9zJmOfn0KLrv15cdqMg5WoKR+r6I0xUeGM005hyrgHeXfK05z511N4cPxEWl7ZnynvzCUrOzvS4bmaVfTGmKhy3pmn8+azjzHjxSc5MbkhYx6fwCXdBzB97iJycmxq5LKwit4YE5VanH8WM196itefeYQ6tWpw+4NP0ebqQcxeuNSmRi4lx5/oEy3RG2OijYjQpkUz5r/6DJOfvJ/EihUZeu+jdOw9hIXLPrapkQNkFb0xJuqJCJe3uZj33nie5x4eTVZ2DgNHPkBq/3+wdMUqS/glcLxZxMfHUSEhIWzHtERvjCmTuLg4ul7elqVvv8R//jWSXZl76DN8DN1vvIOVa76MdHhRy+M4YZ3nBkqR6EUkXkQ+F5F5hbaPFBEVkbrFtNssIl+JyFoRWV3egI0x0SUhIZ5r/n4ZH6ZN5pG7/8HPW3+l500j6XXLXaz5akOkw4s6jjcrrPPcQOkq+hHAYX81ETkB6Aj8XELbtqp6jqo2K2V8xhiXqFihAv17/p3ls6bwr9tuYv33m+hywwj633YfX3/7Q6TDixrhXkYQAkz0IpIMpAKTCr00DhgFWKecMQbwXWQcfF0PVs6Zyl233MCqteu4/Lqbuenuh/j+x5JqwtgX7oXBIfCKfjy+hH5wHJWIdAG2quoXJbRV4D0RWSMig8sUpTHGdapWqczwAb1ZOXcqtw66jqUrVtHumsEM/+dYNm/ZFunwIiYqK3oR6QxsV9U1BbZVAcYA/wzgGC1V9TygEzBURFoXc5zBIrJaRFbv2LEjsOiNMVGvRvVq3DmkP5/MncpN1/Vg/uKPuLTHQEY9PJ6tv22PdHhhF60VfUugi4hsBt4C2gGvAScBX/i3JwOfichxhRur6jb/7+3ALKB5UQdR1Ymq2kxVm9WrV68Mp2KMiWa1a9bg3hE3smLOFPr26Mzb775Hq2438M8nn2P7H7siHV7YRGVFr6qjVTVZVZsAvYAlqtpDVeurahP/9i3Aear6W8G2IlJVRKrnPwYuA74O9kkYY9yjQd06PDRqKMtnvULP1A5MeWcuF1/Zn0eeeZldmXsjHV7IOY43qkfdBEREGopIuv9pA2C5iHwBfArMV9WFwT6mMcZ9ko9vwBP33sayd16mU9uWPDf1bVp07cdTL05l7/4DkQ4vZDxeb/SOowdQ1WWq2rmI7U1U9Q//422qmuJ/vElVz/b/nKGqDwcnbGNMrDi5cSOe+ffdLH7rRVpfeB7/eWkaLbr249kp0/nT44l0eEEXExW9McaUxV9PacJLY//JgtcmcN6Zp/PIhJdp0bU/k96cdXBB7VjgeLOiu6I3xphQO+v003jt6YeY/fI4Tjv5RP711PO06n4902bOJzsnJ9LhlZvHcayiN8YYgAvOPoN3XniC6c8/TsMG9bnrkae5tMdA3pn3Prm57pwLPy8vD29WdvSNujHGmEhqdcG5zHl5HFPHP0T1alW59f4naHfNYN7N+NB1c+Hnry5lXTfGGFOIiNC+VXMWvDaBiY/fR1xcHEPufojL+9zCex+udM3UyPnXGqzrxhhjihEXF0dq+0vIePMFnvn3Xfz5p8MNt/+Lv98wgg//91nUJ/xILDoCluiNMS4UHx9P907tWTZjEk/cexu/79hJ76F3c9VNd/Lp2ui9J9MTgWUEwRK9McbFKiQkcO2VnVg+6xUeunMoP/y0hW6DbqfP8Hv4Yv13kQ7vCI5V9MYYUzaVKlbkhmu6smLOFO4dPoi1674jpd8wBo68nw0bf4x0eAd5/Bdjo3FSM2OMcYXKiYnc3O9qVs55lZFD+vHxqrV07D2EW+55hB9+2hLp8KyP3hhjgqV6tarcNqgPK+dOZej11/Dehytpc9Ugbn/gKX7Z9lvJbxAijlX0xhgTXLVqJDF66AA+mTuVgb2uZPaiJVzSfQCjH/svv+3YGfZ4rKI3xpgQqVu7FvffPoSPZ79Kr65X8MasBbS8sj8PjHuRP3btDlscNo7eGGNC7Pj6dXls9HA+mjmZLpe1YdKbs2jRtT+PPfsKmXv3hfz4HscB7M5YY4wJucaNjmfcv0ay9O2X6HjJRTzzypu06NKP8ZNeZ/+BP0N2XKvojTEmzP7S5ASee+Qe3n/zBVqcfxZPvPAqF3XpxwuvvXOw+g6m/Pe0G6aMMSbMmp56MpOfeoB5U/7L2U1P499Pv0TLK69nyttz8WYFby58x/GSEB9PhYSEoL1nICzRG2OM37ln/o3Xn3mEtIlP0uSEhowZO4FLug/gzdkLyMkp/9TIHm9W2EfcgCV6Y4w5wkXnnUXaxKd4c8Kj1K9Ti5EPjePSqwYya+GScs2F7zhO2LttwBK9McYUSURofdH5vDvlv7zy1ANUTkxk2L2P0bH3ENKXLC/TTJkexyp6Y4yJOiLCZZe24L3Xn+P5R8eQk5vLjaMepFPfoSz5+NNSJXzfwuCW6I0xJirFxcXRpeOlLJn+EuPuH8meffvpO+Jerhx4Gx+vXhvQezher1X0xhgT7RIS4rm682V8MONlHhs9nC2/befqIaO45pa7WPPVhqO29TjesI+hB0v0xhhTJhUrVKBvj858PGsK998+hG82/kiXG0bQ79b7+PqbjUW28VX04b0rFkqR6EUkXkQ+F5F5hbaPFBEVkbrFtLtCRL4VkY0icnd5AzbGmGiSWKkiN17bnRWzX2X0sAGs+XI9l/e5hcF3/ZvvNv102L5uqOhHAId9LxGRE4COwM9FNRCReOBZoBPQFOgtIk3LFqoxxkSvqlUqM+z6XqycO5XbbuzDB5+sod01g/nHfY/z4y9bAd8UCFHbRy8iyUAqMKnQS+OAUUBxl52bAxtVdZOqZgFvAV3LGKsxxkS9pGpVGXlTP1bOeZWb+15F+pLlXNpzIKMeHkfmvn1RPepmPL6Enpe/QUS6AFtV9YujtGsE/FLg+Rb/tiOIyGARWS0iq3fs2BFgWMYYE51q16zBmOGDWDFnCv17/p135mWQuSdKE72IdAa2q+qaAtuqAGOAf5bUvIhtRVb/qjpRVZuparN69eqVFJYxxrhCg7p1+PedQ1k+6xWG3dCLq//eMewxBDKzTkugi4ikAIlAEvAacBLwhYgAJAOfiUhzVS24TtcW4IQCz5OBbcEI3Bhj3KTRcfUZPXRARI5dYkWvqqNVNVlVmwC9gCWq2kNV66tqE//2LcB5hZI8wCrgVBE5SUQq+tvPDe4pGGOMOZqgj6MXkYYikg6gqjnAMGARvhE7b6vqumAf0xhjTPFKNSmyqi4DlhWxvUmBx9uAlALP04H0sgZojDGmfOzOWGOMiXGW6I0xJsZZojfGmBhnid4YY2KcJXpjjIlxUpblsEJNRHYAP5W4Y8nqAn8E4X0ixe3xg/vPwe3xg51DNAhH/CeqapHTCkRlog8WEVmtqs0iHUdZuT1+cP85uD1+sHOIBpGO37pujDEmxlmiN8aYGBfriX5ipAMoJ7fHD+4/B7fHD3YO0SCi8cd0H70xxpjYr+iNMeaYZ4neGGNiXEwkehG5SkTWiUieiDQrsL2jiKwRka/8v9sVeG2hiHzhb/eCfyHziCntOYhIFRGZLyLf+Ns9Frnoy/w3eFhEfhGR/ZGJ+nBlPIfz/ds3ish/xb8STyQcJf46IrJURPaLyIRCba4RkS/97caGP+rDlfEcevv/Bl/6/13XDX/kB2MpVfwiUl1E1hb4+UNExgc9MFV1/Q9wOvBXfFMoNyuw/Vygof/xmfjWuM1/Lcn/W4A0oJebzgGoArT1P64IfAR0ckv8/ucXAccD+yP9/1A5zuFToIX//6MFUfo3qAq0AoYAEwpsrwP8DNTzP38VaB+lf4PiziEB2A7U9T8fC9zvlviLaL8GaB3suEo1H320UtUNAIWLKVX9vMDTdUCiiFRSVa+q7vVvT8CXKCN6VboM5/AnsNS/T5aIfIZvqcaIKOPf4JOi2kRKac8BqI2vYFjpbzcVuBJfwg+7o8R/AFguIn8p1ORk4DtV3eF/ngH0ABaHONRileEcxP9TVUR24lvqdGMYQi1SGeI/SEROBerjK9qCKia6bgLUA/hcVb35G0RkEb5qYB8wI1KBlcIR5wAgIjWBvxPBf6ABKjJ+lyl4Do3wLaOZb4t/m1tsBP4mIk1EJAHfh9QJR28SXVQ1G7gZ+ArfetRNgZcjGlTZ9Qamq7+0DybXVPQikgEcV8RLY1R1TgltzwAeBy4ruF1VLxeRROB1oB3wfpDCLS6OoJ+D/x/om8B/VXVTsGItJoagxx9uQT6Hor6KhPSbYXniL0xVd4vIzcB0IA9Yga/KD6lgnoOIVMCX6M8FNgHPAKOBh8ob51GOGbT4C+kF9C1H+2K5JtGraoeytBORZGAW0E9VfyjifR0RmQt0JcSJPkTnMBH4XlXHlzO8EoXqbxBOQT6HLRzeXZaMr6oMmbLGf5T3exd4F0BEBgO5wXz/Yo4ZzHM4x/+ePwCIyNvA3UF8/yME+28AICJnAwmquibY7w0x3nXj79KYD4xW1Y8LbK8mIsf7HyfgW+P2m4gEWYLizsH/2kNADeDW8EcWmKPF7xbFnYOq/grsE5GL/KNt+gHlqejCTkTq+3/XAm4BJkU2olLbCjQVkfxZGzsCGyIYT1n1xvfNPDQidXU6mD9AN3zVlRf4HVjk334vcABYW+CnPtAAWAV8ie/i2jP4Pk3ddA7J+LoJNhTYPsgt8ftfG+tvk+f/fb+b/gb+15oBXwM/ABPw320eTfH7X9sM7AL2+/dp6t/+JrDe/xPRkWflOIch/n8HX+L7dlLHTfH7X9sE/C1UcdkUCMYYE+NiuuvGGGOMJXpjjIl5luiNMSbGWaI3xpgYZ4neGGNinCV6Y4yJcZbojTEmxv0/7NcBwVPFUTEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop40.0, tr487,wedgePops782238.02, 1074.9, 186184.1, 1161.6, 593817.4, Overedge?1, 1, 0, 0, ,Satisfied?0010,yoyo?0001 \n",
      "   targetWP, latest drx4 are tWP,dr, 582104.5,0.3957, 582104.5,-2.4368, 1163.1,0.002, 582104.5,-0.0545\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 0.3983 7.2082 1074.9098 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 2.43682 401977.4415 186184.0855 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.06285 1155.6347 1161.586 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 1.56829 607483.1542 593817.4408 0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 487, giving up w pop 782238.0220453881\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 488 4.0 3 37.6 2.6113 539643.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 488 9.0 3 37.6 2.7035 539643.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 488 10.0 3 37.6 2.0374 539643.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 488 1 538766.6426915416 3.9348\n",
      "8 yoyos for tract,wedge,wedgePop,r= 488 1 1090127.0272581652 8.6463\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 488 25.0 1 37.6 6.2905 1090127.0\n",
      "8 yoyos for tract,wedge,wedgePop,r= 488 1 1090127.0272581652 6.2905\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 488 26.0 1 37.6 5.1126 1090127.0\n",
      "8 yoyos for tract,wedge,wedgePop,r= 488 1 1090127.0272581652 5.1126\n",
      "8 yoyos for tract,wedge,wedgePop,r= 488 1 1088806.6107737424 4.5237\n",
      "8 yoyos for tract,wedge,wedgePop,r= 488 1 991087.18990547 4.2292\n",
      "8 yoyos for tract,wedge,wedgePop,r= 488 1 819103.7293115547 4.082\n",
      "9 yoyos for tract,wedge,wedgePop,r= 488 1 599598.940463362 4.0084\n",
      "loop31.0, tr488,wedgePops751957.66, 7527.1, 695768.7, 9137.4, 39524.5, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0900 \n",
      "   targetWP, latest drx4 are tWP,dr, 714328.8,1.44, 714328.8,0.0368, 9146.2,0.0003, 714328.8,-0.502\n",
      "9 yoyos for tract,wedge,wedgePop,r= 488 1 695768.6621436713 4.0452\n",
      "loop32.0, tr488,wedgePops813641.27, 7527.1, 757452.3, 9137.4, 39524.5, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0900 \n",
      "   targetWP, latest drx4 are tWP,dr, 714328.8,1.44, 714328.8,0.0184, 9146.2,0.0003, 714328.8,-0.502\n",
      "10 yoyos for tract,wedge,wedgePop,r= 488 1 757452.2715640375 4.0636\n",
      "loop33.0, tr488,wedgePops782209.46, 7527.1, 726020.5, 9137.4, 39524.5, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?01000 \n",
      "   targetWP, latest drx4 are tWP,dr, 714328.8,1.44, 714328.8,-0.0092, 9146.2,0.0003, 714328.8,-0.502\n",
      "10 yoyos for tract,wedge,wedgePop,r= 488 1 726020.4606349331 4.0544\n",
      "loop34.0, tr488,wedgePops767268.89, 7527.1, 711079.9, 9137.4, 39524.5, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?01000 \n",
      "   targetWP, latest drx4 are tWP,dr, 714328.8,1.44, 714328.8,-0.0046, 9146.2,0.0003, 714328.8,-0.502\n",
      "7 yoyos for tract,wedge,wedgePop,r= 489 1 434841.35978989763 3.5746\n",
      "8 yoyos for tract,wedge,wedgePop,r= 489 1 1063533.7888675586 8.051\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 489 19.0 1 36.0 5.8128 1063533.8\n",
      "8 yoyos for tract,wedge,wedgePop,r= 489 1 1063533.7888675586 5.8128\n",
      "8 yoyos for tract,wedge,wedgePop,r= 489 1 1063095.0140989218 4.6937\n",
      "8 yoyos for tract,wedge,wedgePop,r= 489 1 978176.2913226326 4.1342\n",
      "9 yoyos for tract,wedge,wedgePop,r= 489 1 551455.8527566891 3.8544\n",
      "10 yoyos for tract,wedge,wedgePop,r= 489 1 821078.95008211 3.9943\n",
      "11 yoyos for tract,wedge,wedgePop,r= 489 1 635515.9366186841 3.9243\n",
      "12 yoyos for tract,wedge,wedgePop,r= 489 1 726626.4234312323 3.9593\n",
      "7 yoyos for tract,wedge,wedgePop,r= 492 3 198908.12790069717 0.8035\n",
      "8 yoyos for tract,wedge,wedgePop,r= 492 3 5921.60416003727 0.4018\n",
      "9 yoyos for tract,wedge,wedgePop,r= 492 3 238171.89837503972 0.8486\n",
      "10 yoyos for tract,wedge,wedgePop,r= 492 3 22498.385893499566 0.6252\n",
      "10 yoyos for tract,wedge,wedgePop,r= 492 3 107365.85892173715 0.7369\n",
      "10 yoyos for tract,wedge,wedgePop,r= 492 3 184858.77318835474 0.7927\n",
      "11 yoyos for tract,wedge,wedgePop,r= 492 3 218842.1501936744 0.8207\n",
      "11 yoyos for tract,wedge,wedgePop,r= 492 3 203038.8230924467 0.8067\n",
      "7 yoyos for tract,wedge,wedgePop,r= 493 0 240670.39849413675 2.0741\n",
      "8 yoyos for tract,wedge,wedgePop,r= 493 0 170431.31908934354 1.0371\n",
      "9 yoyos for tract,wedge,wedgePop,r= 493 0 236637.95366794703 1.8996\n",
      "10 yoyos for tract,wedge,wedgePop,r= 493 0 178011.9413265965 1.4683\n",
      "11 yoyos for tract,wedge,wedgePop,r= 493 0 216931.2241667193 1.6839\n",
      "12 yoyos for tract,wedge,wedgePop,r= 493 0 180340.59276389555 1.5761\n",
      "we have 2 non-opposing shorted wedges for tract no 497\n",
      "I am working on tract number 500 of 1784 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 509 2.0 0 90.0 1.1333 360108.2\n",
      "we have 2 non-opposing shorted wedges for tract no 517\n",
      "I am working on tract number 520 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 537 3 436787.7648355264 2.045\n",
      "8 yoyos for tract,wedge,wedgePop,r= 537 3 222965.38947000282 1.0225\n",
      "9 yoyos for tract,wedge,wedgePop,r= 537 3 436855.89270052454 2.0686\n",
      "10 yoyos for tract,wedge,wedgePop,r= 537 3 267285.7076656732 1.5455\n",
      "11 yoyos for tract,wedge,wedgePop,r= 537 3 400241.3654750156 1.8071\n",
      "11 yoyos for tract,wedge,wedgePop,r= 537 3 352011.4318573157 1.6763\n",
      "12 yoyos for tract,wedge,wedgePop,r= 537 3 277801.8087346758 1.6109\n",
      "12 yoyos for tract,wedge,wedgePop,r= 537 3 291700.2377448489 1.6436\n",
      "7 yoyos for tract,wedge,wedgePop,r= 539 1 640857.2706702892 1.9518\n",
      "8 yoyos for tract,wedge,wedgePop,r= 539 1 118418.43168275373 0.9759\n",
      "9 yoyos for tract,wedge,wedgePop,r= 539 1 640841.743470562 1.9512\n",
      "9 yoyos for tract,wedge,wedgePop,r= 539 1 469877.5910584483 1.4636\n",
      "10 yoyos for tract,wedge,wedgePop,r= 539 1 160394.21345260955 1.2197\n",
      "10 yoyos for tract,wedge,wedgePop,r= 539 1 277004.68736167724 1.3416\n",
      "11 yoyos for tract,wedge,wedgePop,r= 539 1 371304.2233641025 1.4026\n",
      "12 yoyos for tract,wedge,wedgePop,r= 539 1 315758.2136314422 1.3721\n",
      "I am working on tract number 540 of 1784 tracts\n",
      "I am working on tract number 560 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 563 1 358862.3291274489 2.6103\n",
      "8 yoyos for tract,wedge,wedgePop,r= 563 1 273622.74319935404 1.3051\n",
      "9 yoyos for tract,wedge,wedgePop,r= 563 1 366614.8458708906 2.6261\n",
      "10 yoyos for tract,wedge,wedgePop,r= 563 1 320489.5616178133 1.9656\n",
      "10 yoyos for tract,wedge,wedgePop,r= 563 1 325776.6460679815 2.2958\n",
      "10 yoyos for tract,wedge,wedgePop,r= 563 1 334530.8220963769 2.461\n",
      "10 yoyos for tract,wedge,wedgePop,r= 563 1 342679.4651087909 2.5435\n",
      "10 yoyos for tract,wedge,wedgePop,r= 563 1 347773.0719634229 2.5848\n",
      "we have 2 non-opposing shorted wedges for tract no 575\n",
      "I am working on tract number 580 of 1784 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 586 2.0 2 90.0 0.9572 99671.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 587 4.0 1 90.0 0.864 205118.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 587 5.0 1 90.0 0.824 205118.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 587 1 203629.5304695111 0.5945\n",
      "8 yoyos for tract,wedge,wedgePop,r= 587 1 150546.46769829834 0.2973\n",
      "9 yoyos for tract,wedge,wedgePop,r= 587 1 205089.64771605394 0.698\n",
      "10 yoyos for tract,wedge,wedgePop,r= 587 1 165183.8531113631 0.4976\n",
      "11 yoyos for tract,wedge,wedgePop,r= 587 1 204008.65605989087 0.5978\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 588 2.0 3 90.0 0.5896 170359.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 588 3.0 3 90.0 0.6456 170359.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 588 4.0 3 90.0 0.7069 170359.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 588 5.0 3 90.0 0.7741 170359.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 588 6.0 3 90.0 0.8477 170359.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 588 7.0 3 90.0 0.9283 170359.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 588 8.0 3 90.0 1.0165 170359.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 588 9.0 3 90.0 1.1131 170359.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 588 10.0 3 90.0 1.2189 170359.8\n",
      "we have 2 non-opposing shorted wedges for tract no 589\n",
      "we have 2 non-opposing shorted wedges for tract no 591\n",
      "we have 2 non-opposing shorted wedges for tract no 593\n",
      "we have 2 non-opposing shorted wedges for tract no 595\n",
      "I am working on tract number 600 of 1784 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 608\n",
      "we have 2 non-opposing shorted wedges for tract no 609\n",
      "I am working on tract number 620 of 1784 tracts\n",
      "I am working on tract number 640 of 1784 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 656 5.0 0 90.0 3.3784 324294.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 657 3 603388.6601650945 2.9923\n",
      "8 yoyos for tract,wedge,wedgePop,r= 657 3 15175.926724871155 1.4961\n",
      "9 yoyos for tract,wedge,wedgePop,r= 657 3 874710.4324400525 4.3097\n",
      "9 yoyos for tract,wedge,wedgePop,r= 657 3 520557.75426321424 2.9029\n",
      "10 yoyos for tract,wedge,wedgePop,r= 657 3 36067.825529491645 2.1995\n",
      "10 yoyos for tract,wedge,wedgePop,r= 657 3 67156.44528246627 2.5512\n",
      "10 yoyos for tract,wedge,wedgePop,r= 657 3 177608.30839126487 2.7271\n",
      "10 yoyos for tract,wedge,wedgePop,r= 657 3 362672.6607076153 2.815\n",
      "11 yoyos for tract,wedge,wedgePop,r= 657 3 436234.52945321694 2.859\n",
      "11 yoyos for tract,wedge,wedgePop,r= 657 3 398815.3927561839 2.837\n",
      "11 yoyos for tract,wedge,wedgePop,r= 657 3 380483.2883469809 2.826\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 659 5.0 2 90.0 2.7654 564648.9\n",
      "I am working on tract number 660 of 1784 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 670 5.0 1 90.0 2.5319 229486.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 674 6.0 0 90.0 3.3201 237341.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 674 7.0 0 90.0 2.8266 237341.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 674 8.0 0 90.0 2.4064 237341.1\n",
      "I am working on tract number 680 of 1784 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 692\n",
      "we have 2 non-opposing shorted wedges for tract no 694\n",
      "I am working on tract number 700 of 1784 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 700 3.0 1 36.0 0.7383 378795.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 700 4.0 1 36.0 0.7241 378795.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 703 3.0 0 90.0 0.4186 246379.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 703 4.0 0 90.0 0.3459 246379.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 703 5.0 0 90.0 0.2858 246379.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 703 6.0 0 90.0 0.2362 246379.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 703 1 353693.4652206054 3.2906\n",
      "8 yoyos for tract,wedge,wedgePop,r= 703 1 86149.14864839707 1.6453\n",
      "9 yoyos for tract,wedge,wedgePop,r= 703 1 319295.90776396263 3.1204\n",
      "10 yoyos for tract,wedge,wedgePop,r= 703 1 142777.32021447673 2.3829\n",
      "11 yoyos for tract,wedge,wedgePop,r= 703 1 286366.80649515893 2.7516\n",
      "11 yoyos for tract,wedge,wedgePop,r= 703 1 258253.57287483802 2.5673\n",
      "11 yoyos for tract,wedge,wedgePop,r= 703 1 207101.7846644194 2.4751\n",
      "12 yoyos for tract,wedge,wedgePop,r= 703 1 170542.2264819157 2.429\n",
      "12 yoyos for tract,wedge,wedgePop,r= 703 1 187627.76336843436 2.452\n",
      "7 yoyos for tract,wedge,wedgePop,r= 707 1 432326.76897404104 2.905\n",
      "8 yoyos for tract,wedge,wedgePop,r= 707 1 216111.63619138862 1.4525\n",
      "9 yoyos for tract,wedge,wedgePop,r= 707 1 651895.0795457753 3.9171\n",
      "10 yoyos for tract,wedge,wedgePop,r= 707 1 272014.1802402996 2.6848\n",
      "11 yoyos for tract,wedge,wedgePop,r= 707 1 558241.8097709505 3.3009\n",
      "11 yoyos for tract,wedge,wedgePop,r= 707 1 460257.36365837127 2.9929\n",
      "11 yoyos for tract,wedge,wedgePop,r= 707 1 386158.9777356405 2.8388\n",
      "12 yoyos for tract,wedge,wedgePop,r= 707 1 311445.7890606082 2.7618\n",
      "12 yoyos for tract,wedge,wedgePop,r= 707 1 347309.9023704013 2.8003\n",
      "12 yoyos for tract,wedge,wedgePop,r= 707 1 365309.63426502876 2.8196\n",
      "I am working on tract number 720 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 736 1 906336.3882143733 3.6939\n",
      "7 yoyos for tract,wedge,wedgePop,r= 736 1 565288.2490536089 1.847\n",
      "8 yoyos for tract,wedge,wedgePop,r= 736 1 50583.25256211066 0.9235\n",
      "9 yoyos for tract,wedge,wedgePop,r= 736 1 419925.7949496386 1.6534\n",
      "10 yoyos for tract,wedge,wedgePop,r= 736 1 61523.50293427153 1.2884\n",
      "10 yoyos for tract,wedge,wedgePop,r= 736 1 140616.31163851797 1.4709\n",
      "11 yoyos for tract,wedge,wedgePop,r= 736 1 277516.64266512287 1.5622\n",
      "11 yoyos for tract,wedge,wedgePop,r= 736 1 197426.87890603818 1.5165\n",
      "12 yoyos for tract,wedge,wedgePop,r= 736 1 169840.3484761896 1.4937\n",
      "12 yoyos for tract,wedge,wedgePop,r= 736 1 184524.60940859804 1.5051\n",
      "7 yoyos for tract,wedge,wedgePop,r= 738 0 610655.1494412691 2.5642\n",
      "8 yoyos for tract,wedge,wedgePop,r= 738 0 63771.48277239641 1.2821\n",
      "9 yoyos for tract,wedge,wedgePop,r= 738 0 617938.0843105444 2.6462\n",
      "9 yoyos for tract,wedge,wedgePop,r= 738 0 440524.9869599817 1.9641\n",
      "9 yoyos for tract,wedge,wedgePop,r= 738 0 324460.88088991126 1.6231\n",
      "10 yoyos for tract,wedge,wedgePop,r= 738 0 125731.46511050707 1.4526\n",
      "11 yoyos for tract,wedge,wedgePop,r= 738 0 245413.47814258345 1.5379\n",
      "12 yoyos for tract,wedge,wedgePop,r= 738 0 185718.1306497706 1.4952\n",
      "13 yoyos for tract,wedge,wedgePop,r= 738 0 215648.20632533022 1.5165\n",
      "13 yoyos for tract,wedge,wedgePop,r= 738 0 201906.2265322478 1.5059\n",
      "I am working on tract number 740 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 747 1 276818.6531633683 3.7629\n",
      "8 yoyos for tract,wedge,wedgePop,r= 747 1 52302.748111189634 1.8815\n",
      "9 yoyos for tract,wedge,wedgePop,r= 747 1 276057.7690520189 3.7502\n",
      "10 yoyos for tract,wedge,wedgePop,r= 747 1 105375.16914456591 2.8159\n",
      "11 yoyos for tract,wedge,wedgePop,r= 747 1 242181.1504930657 3.283\n",
      "12 yoyos for tract,wedge,wedgePop,r= 747 1 131802.5222989817 3.0495\n",
      "13 yoyos for tract,wedge,wedgePop,r= 747 1 209209.20979826746 3.1662\n",
      "14 yoyos for tract,wedge,wedgePop,r= 747 1 165403.40109668317 3.1078\n",
      "14 yoyos for tract,wedge,wedgePop,r= 747 1 188612.89819947683 3.137\n",
      "15 yoyos for tract,wedge,wedgePop,r= 747 1 198964.0450982924 3.1516\n",
      "we have 2 non-opposing shorted wedges for tract no 748\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 749 13.0 3 90.0 2.6057 356922.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 749 14.0 3 90.0 2.5756 356922.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 749 15.0 3 90.0 2.5459 356922.4\n",
      "we have 2 non-opposing shorted wedges for tract no 751\n",
      "we have 2 non-opposing shorted wedges for tract no 753\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 755 2.0 0 90.0 0.7974 165862.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 755 3.0 0 90.0 0.8903 165862.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 755 4.0 0 90.0 0.994 165862.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 755 5.0 0 90.0 1.1098 165862.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 755 6.0 0 90.0 1.2392 165862.9\n",
      "I am working on tract number 760 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 760 2 214292.54522108566 4.8042\n",
      "8 yoyos for tract,wedge,wedgePop,r= 760 2 75336.72119883267 2.4021\n",
      "9 yoyos for tract,wedge,wedgePop,r= 760 2 230968.09098497196 4.8127\n",
      "10 yoyos for tract,wedge,wedgePop,r= 760 2 113803.62990826016 3.6074\n",
      "10 yoyos for tract,wedge,wedgePop,r= 760 2 126257.5698741 4.2101\n",
      "10 yoyos for tract,wedge,wedgePop,r= 760 2 138793.32410736452 4.5114\n",
      "10 yoyos for tract,wedge,wedgePop,r= 760 2 160584.2568741105 4.6621\n",
      "10 yoyos for tract,wedge,wedgePop,r= 760 2 173550.82827989216 4.7374\n",
      "10 yoyos for tract,wedge,wedgePop,r= 760 2 182554.2456289849 4.7751\n",
      "11 yoyos for tract,wedge,wedgePop,r= 760 2 197344.98275709085 4.7939\n",
      "12 yoyos for tract,wedge,wedgePop,r= 760 2 186669.41858206334 4.7845\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 771 4.0 1 90.0 0.7709 107610.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 771 5.0 1 90.0 1.1616 107610.4\n",
      "I am working on tract number 780 of 1784 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 786 2.0 3 90.0 0.367 217879.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 786 3.0 3 90.0 0.3341 217879.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 786 4.0 3 90.0 0.3042 217879.7\n",
      "we have 2 non-opposing shorted wedges for tract no 791\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 793 2.0 2 90.0 0.4745 294280.4\n",
      "I am working on tract number 800 of 1784 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 801\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 803 4.0 0 90.0 0.4689 413456.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 804 2.0 1 90.0 0.4277 348080.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 804 5.0 1 90.0 0.5708 387131.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 804 6.0 1 90.0 0.5188 387131.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 804 7.0 1 90.0 0.4715 387131.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 811 3.0 0 90.0 1.0685 343653.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 811 4.0 0 90.0 0.6657 343653.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 812 2.0 2 90.0 0.493 448885.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 814 5.0 0 90.0 0.4117 213262.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 814 6.0 0 90.0 0.3811 213262.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 814 7.0 0 90.0 0.3527 213262.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 814 8.0 0 90.0 0.3265 213262.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 814 9.0 0 90.0 0.3022 213262.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 815 3.0 3 90.0 0.5948 379416.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 815 4.0 3 90.0 0.5377 379416.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 815 5.0 3 90.0 0.486 379416.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 816 1 464447.58070779423 3.3786\n",
      "8 yoyos for tract,wedge,wedgePop,r= 816 1 180053.97381364426 1.6893\n",
      "9 yoyos for tract,wedge,wedgePop,r= 816 1 429947.202967067 3.195\n",
      "10 yoyos for tract,wedge,wedgePop,r= 816 1 226643.43886934788 2.4422\n",
      "11 yoyos for tract,wedge,wedgePop,r= 816 1 394836.0658745208 2.8186\n",
      "11 yoyos for tract,wedge,wedgePop,r= 816 1 357169.7787757382 2.6304\n",
      "I am working on tract number 820 of 1784 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 820\n",
      "I am working on tract number 840 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 848 2 329534.3065814212 2.2728\n",
      "8 yoyos for tract,wedge,wedgePop,r= 848 2 47304.59595304716 1.1364\n",
      "9 yoyos for tract,wedge,wedgePop,r= 848 2 339559.1634058297 2.3252\n",
      "10 yoyos for tract,wedge,wedgePop,r= 848 2 84317.48249805876 1.7308\n",
      "10 yoyos for tract,wedge,wedgePop,r= 848 2 145831.8800119398 2.028\n",
      "11 yoyos for tract,wedge,wedgePop,r= 848 2 311130.98505438835 2.1766\n",
      "11 yoyos for tract,wedge,wedgePop,r= 848 2 251317.5034094472 2.1023\n",
      "we have 2 non-opposing shorted wedges for tract no 853\n",
      "we have 2 non-opposing shorted wedges for tract no 854\n",
      "7 yoyos for tract,wedge,wedgePop,r= 854 0 543399.5432955327 1.6886\n",
      "8 yoyos for tract,wedge,wedgePop,r= 854 0 19956.298907080665 0.8443\n",
      "9 yoyos for tract,wedge,wedgePop,r= 854 0 592299.2913470713 2.1111\n",
      "10 yoyos for tract,wedge,wedgePop,r= 854 0 177620.05197760236 1.4777\n",
      "11 yoyos for tract,wedge,wedgePop,r= 854 0 565074.1855157285 1.7944\n",
      "11 yoyos for tract,wedge,wedgePop,r= 854 0 519786.83820220584 1.636\n",
      "11 yoyos for tract,wedge,wedgePop,r= 854 0 396646.935604397 1.5569\n",
      "12 yoyos for tract,wedge,wedgePop,r= 854 0 288493.19164821075 1.5173\n",
      "12 yoyos for tract,wedge,wedgePop,r= 854 0 344475.50595786725 1.5371\n",
      "12 yoyos for tract,wedge,wedgePop,r= 854 0 371980.8893507522 1.547\n",
      "13 yoyos for tract,wedge,wedgePop,r= 854 0 384596.3404131351 1.5519\n",
      "I am working on tract number 860 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 878 1 382013.4828326343 2.5013\n",
      "8 yoyos for tract,wedge,wedgePop,r= 878 1 70040.24341744115 1.2507\n",
      "9 yoyos for tract,wedge,wedgePop,r= 878 1 384171.7181185106 2.8551\n",
      "10 yoyos for tract,wedge,wedgePop,r= 878 1 125958.73169438759 2.0529\n",
      "11 yoyos for tract,wedge,wedgePop,r= 878 1 373578.74921193684 2.454\n",
      "12 yoyos for tract,wedge,wedgePop,r= 878 1 146529.4507556458 2.2535\n",
      "13 yoyos for tract,wedge,wedgePop,r= 878 1 267930.5094100557 2.3537\n",
      "13 yoyos for tract,wedge,wedgePop,r= 878 1 203042.8960059951 2.3036\n",
      "14 yoyos for tract,wedge,wedgePop,r= 878 1 171125.31534045897 2.2785\n",
      "14 yoyos for tract,wedge,wedgePop,r= 878 1 185359.77677586942 2.2911\n",
      "I am working on tract number 880 of 1784 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 885 3.0 1 36.0 0.7479 403903.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 896 3 233004.98874053272 2.3836\n",
      "8 yoyos for tract,wedge,wedgePop,r= 896 3 112551.35922050165 1.1918\n",
      "9 yoyos for tract,wedge,wedgePop,r= 896 3 232113.79917607174 2.2918\n",
      "10 yoyos for tract,wedge,wedgePop,r= 896 3 153773.08855375415 1.7418\n",
      "11 yoyos for tract,wedge,wedgePop,r= 896 3 223674.14217850746 2.0168\n",
      "12 yoyos for tract,wedge,wedgePop,r= 896 3 158907.3752121213 1.8793\n",
      "12 yoyos for tract,wedge,wedgePop,r= 896 3 183504.55317798135 1.948\n",
      "7 yoyos for tract,wedge,wedgePop,r= 898 0 217004.29977843247 2.355\n",
      "8 yoyos for tract,wedge,wedgePop,r= 898 0 60140.85599929877 1.1775\n",
      "9 yoyos for tract,wedge,wedgePop,r= 898 0 413168.6889848043 2.5438\n",
      "10 yoyos for tract,wedge,wedgePop,r= 898 0 98268.22464195872 1.8606\n",
      "10 yoyos for tract,wedge,wedgePop,r= 898 0 111219.8563747971 2.2022\n",
      "11 yoyos for tract,wedge,wedgePop,r= 898 0 245097.73807362583 2.373\n",
      "12 yoyos for tract,wedge,wedgePop,r= 898 0 120761.37650768289 2.2876\n",
      "12 yoyos for tract,wedge,wedgePop,r= 898 0 169694.81989538652 2.3303\n",
      "13 yoyos for tract,wedge,wedgePop,r= 898 0 211221.06691499762 2.3516\n",
      "I am working on tract number 900 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 901 3 707155.7968822046 2.6595\n",
      "8 yoyos for tract,wedge,wedgePop,r= 901 3 508195.97819254664 1.3297\n",
      "9 yoyos for tract,wedge,wedgePop,r= 901 3 767373.7397979058 2.8248\n",
      "10 yoyos for tract,wedge,wedgePop,r= 901 3 557533.7095977974 2.0773\n",
      "10 yoyos for tract,wedge,wedgePop,r= 901 3 588404.5545872895 2.451\n",
      "11 yoyos for tract,wedge,wedgePop,r= 901 3 704033.8706809034 2.6379\n",
      "12 yoyos for tract,wedge,wedgePop,r= 901 3 668243.0940947864 2.5445\n",
      "13 yoyos for tract,wedge,wedgePop,r= 901 3 694736.5559337125 2.5912\n",
      "13 yoyos for tract,wedge,wedgePop,r= 901 3 687059.052348359 2.5678\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 915 4.0 0 90.0 1.3681 225567.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 915 5.0 0 90.0 1.2123 225567.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 915 6.0 0 90.0 1.0743 225567.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 915 1 641230.1492576345 2.8766\n",
      "I am working on tract number 920 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 923 3 408457.3887229778 1.7979\n",
      "8 yoyos for tract,wedge,wedgePop,r= 923 3 177753.65744909277 0.8989\n",
      "9 yoyos for tract,wedge,wedgePop,r= 923 3 408732.40788393887 1.8022\n",
      "10 yoyos for tract,wedge,wedgePop,r= 923 3 233840.85622109787 1.3505\n",
      "11 yoyos for tract,wedge,wedgePop,r= 923 3 367357.9777917266 1.5764\n",
      "12 yoyos for tract,wedge,wedgePop,r= 923 3 270329.5570425533 1.4635\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 924 7.0 1 36.0 3.1896 404047.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 924 8.0 1 36.0 3.0344 404047.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 924 13.0 1 36.0 3.3618 404047.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 924 13.0 3 36.0 5.2511 724964.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 924 14.0 1 36.0 3.1982 404047.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 925 1 680147.4748713092 3.4582\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 928 10.0 3 36.0 3.3475 414777.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 931 10.0 1 64.7 2.9031 440827.0\n",
      "I am working on tract number 940 of 1784 tracts\n",
      "I am working on tract number 960 of 1784 tracts\n",
      "I am working on tract number 980 of 1784 tracts\n",
      "I am working on tract number 1000 of 1784 tracts\n",
      "I am working on tract number 1020 of 1784 tracts\n",
      "I am working on tract number 1040 of 1784 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1055 3.0 0 90.0 1.1641 223039.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1058 5.0 0 90.0 1.2768 233754.6\n",
      "I am working on tract number 1060 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1060 3 235955.10784008316 1.6309\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1060 3 108211.61663864108 0.8154\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1060 3 237264.90979840123 1.6477\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1060 3 143961.2470366198 1.2316\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1060 3 205080.92887524178 1.4397\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1060 3 152625.09809778177 1.3356\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1060 3 170882.6985235801 1.3876\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1060 3 186035.9412951259 1.4136\n",
      "I am working on tract number 1080 of 1784 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1094\n",
      "I am working on tract number 1100 of 1784 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1101 3.0 3 90.0 1.687 225042.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1101 4.0 3 90.0 1.4977 225042.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1101 5.0 3 90.0 1.3296 225042.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1102 3 278769.12055555615 1.2962\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1102 3 65771.93067648783 0.6481\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1102 3 269940.458215881 1.2221\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1102 3 108001.5879481782 0.9351\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1102 3 246844.36908932723 1.0786\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1102 3 211966.61641813006 1.0069\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1102 3 152531.3741269779 0.971\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1110 0 342670.9076885057 1.7877\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1110 0 109358.45063209781 0.8939\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1110 0 342675.07062011864 1.7947\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1110 0 243886.568087154 1.3443\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1110 0 123844.14599914197 1.1191\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1110 0 141401.27670081114 1.2317\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1110 0 157684.72834018272 1.288\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1110 0 183159.8369932384 1.3161\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1110 0 211444.81583547842 1.3302\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1111 3 238921.92159813686 1.2608\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1111 3 183792.47139365404 0.6304\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1111 3 239542.17896679125 1.3958\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1111 3 186912.73385943554 1.0131\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1111 3 237464.84023391802 1.2044\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1111 3 222939.11056776938 1.1087\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1113 3 532184.9755548824 3.4296\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1113 3 340667.7642111719 1.7148\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1113 3 598899.123002816 3.5191\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1113 3 436916.4418269183 2.6169\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1113 3 459298.5957032258 3.068\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1113 3 487354.67729058344 3.2936\n",
      "we have 2 non-opposing shorted wedges for tract no 1117\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1117 0 598214.8559892555 2.2748\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1117 0 45943.82028148137 1.1374\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1117 0 637777.265007026 2.9819\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1117 0 560697.720014918 2.0596\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1117 0 61756.01275389944 1.5985\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1117 0 256218.68154586718 1.8291\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1117 0 500557.56171847426 1.9444\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1117 0 400935.6138660911 1.8867\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1117 0 331837.402215014 1.8579\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1117 0 368658.74407919793 1.8723\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1117 0 385406.59220473515 1.8795\n",
      "I am working on tract number 1120 of 1784 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1134 6.0 3 64.2 3.3559 639250.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1138 13.0 3 71.6 4.2365 698946.4\n",
      "I am working on tract number 1140 of 1784 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1144 7.0 3 60.1 3.0406 629323.1\n",
      "we have 2 non-opposing shorted wedges for tract no 1146\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1147 11.0 3 64.2 3.2114 622944.4\n",
      "I am working on tract number 1160 of 1784 tracts\n",
      "I am working on tract number 1180 of 1784 tracts\n",
      "I am working on tract number 1200 of 1784 tracts\n",
      "I am working on tract number 1220 of 1784 tracts\n",
      "I am working on tract number 1240 of 1784 tracts\n",
      "I am working on tract number 1260 of 1784 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1260 5.0 3 61.3 2.5224 595083.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1267 1 510646.0117035344 3.543\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1267 1 202751.09328335832 1.7715\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1267 1 462711.1820417665 3.3101\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1267 1 287337.8315320194 2.5408\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1267 1 424820.6421950188 2.9255\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1267 1 399821.77229831996 2.7331\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1267 1 362062.3470804738 2.637\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1276 1 555071.1222755766 1.955\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1276 1 28474.457010707818 0.9775\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1276 1 555108.0372896747 1.9564\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1276 1 370528.74780395295 1.4669\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1276 1 70280.03723877139 1.2222\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1276 1 174307.94411150055 1.3446\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1276 1 261668.86558109237 1.4058\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1276 1 211725.35073477472 1.3752\n",
      "I am working on tract number 1280 of 1784 tracts\n",
      "I am working on tract number 1300 of 1784 tracts\n",
      "I am working on tract number 1320 of 1784 tracts\n",
      "I am working on tract number 1340 of 1784 tracts\n",
      "I am working on tract number 1360 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1368 3 430411.412364507 2.7056\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1368 3 32002.07954026264 1.3528\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1368 3 436058.345769653 2.7238\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1368 3 262674.782402384 2.0383\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1368 3 65645.54232179123 1.6955\n",
      "I am working on tract number 1380 of 1784 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1386 6.0 2 90.0 4.0324 277806.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1387 16.0 3 90.0 3.3081 680567.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1391 3 539785.2219946402 2.9046\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1391 3 30246.51007722458 1.4523\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1391 3 668059.6319088361 3.1285\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1391 3 79682.61690272356 2.2904\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1391 3 378952.8583241622 2.7095\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1391 3 130937.55208297353 2.4999\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1391 3 189652.18744143166 2.6047\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1391 3 272095.3308264782 2.6571\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1391 3 325971.52124792093 2.6833\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1391 3 297190.0841389489 2.6702\n",
      "loop31.0, tr1391,wedgePops776920.6, 107353.4, 278049.7, 107427.8, 284089.7, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?03613 \n",
      "   targetWP, latest drx4 are tWP,dr, 277909.9,1.3866, 277909.9,-0.0006, 107353.4,0.0021, 277909.9,-0.0065\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1391 3 284089.7321333765 2.6636\n",
      "loop32.0, tr1391,wedgePops770871.95, 107353.4, 278049.7, 107427.8, 278041.1, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?03613 \n",
      "   targetWP, latest drx4 are tWP,dr, 277909.9,1.3866, 277909.9,-0.0006, 107353.4,0.0021, 277909.9,-0.0033\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1392 1 321921.30656400445 1.4175\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1392 1 38186.23610132787 0.7088\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1392 1 337422.14054425753 1.6392\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1392 1 120090.40116935689 1.174\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1392 1 320432.70704552636 1.4066\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1392 1 232209.2398401854 1.2903\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1392 1 167941.39528384747 1.2321\n",
      "I am working on tract number 1400 of 1784 tracts\n",
      "I am working on tract number 1420 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1432 2 239233.33179982292 0.8657\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1432 2 75809.80718898113 0.4328\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1432 2 239233.57011783874 0.8657\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1432 2 114959.9360043603 0.6492\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1432 2 224113.7993532967 0.7575\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1432 2 166117.43294864992 0.7034\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1432 2 207854.93243106166 0.7304\n",
      "14 yoyos for tract,wedge,wedgePop,r= 1432 2 186381.99062623287 0.7169\n",
      "15 yoyos for tract,wedge,wedgePop,r= 1432 2 197424.60024209064 0.7236\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1435 3 473536.7544885188 3.1032\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1435 3 40068.6329417245 1.5516\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1435 3 473349.19555904856 3.1019\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1435 3 333869.34778865415 2.3267\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1435 3 97685.14257765666 1.9392\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1435 3 240081.3853050118 2.133\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1435 3 184898.0664800838 2.0361\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1435 3 222122.33677462125 2.0845\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1435 3 204880.5560428123 2.0603\n",
      "I am working on tract number 1440 of 1784 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1441\n",
      "we have 2 non-opposing shorted wedges for tract no 1458\n",
      "I am working on tract number 1460 of 1784 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1461\n",
      "we have 2 non-opposing shorted wedges for tract no 1463\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1464 6.0 0 90.0 0.9018 96521.1\n",
      "I am working on tract number 1480 of 1784 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1482 7.0 0 90.0 0.7937 92746.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1483 2.0 2 90.0 1.1354 204726.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1483 3.0 2 90.0 1.0844 204726.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1483 4.0 2 90.0 1.0356 204726.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1483 5.0 2 90.0 0.989 204726.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1483 6.0 2 90.0 0.9445 204726.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1483 7.0 2 90.0 0.902 204726.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1483 8.0 2 90.0 0.8614 204726.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1483 9.0 2 90.0 0.8227 204726.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1483 10.0 2 90.0 0.7857 204726.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1483 11.0 2 90.0 0.7503 204726.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1483 12.0 2 90.0 0.7166 204726.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1483 13.0 2 90.0 0.6844 204726.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1483 14.0 2 90.0 0.6536 204726.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1484 1 410800.3185592156 4.0408\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1484 1 94076.83060746215 2.0204\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1484 1 859120.0172578956 4.1708\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1484 1 141918.03674954048 3.0956\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1484 1 170707.64679543627 3.6332\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1484 1 243871.97062961 3.902\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1484 1 401390.2903491474 4.0364\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1484 1 291419.64602464996 3.9692\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1484 1 332546.8976130138 4.0028\n",
      "we have 2 non-opposing shorted wedges for tract no 1485\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1486 1 3045668.5360530056 12.9903\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1486 16.0 1 36.0 7.8876 3045668.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1486 1 3045668.5360530056 7.8876\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1486 1 3023853.8439624975 5.3363\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1486 1 1403855.9037706235 4.0607\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1486 1 56580.249571585795 3.4228\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1486 1 132814.18833727587 3.7418\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1486 1 485701.99362486426 3.9012\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1486 1 945000.9301510421 3.9809\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1486 1 683422.6149155484 3.9411\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1486 1 805492.2895701575 3.961\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1486 1 738983.1255028667 3.9511\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1486 1 770917.1280611542 3.956\n",
      "we have 2 non-opposing shorted wedges for tract no 1494\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1499 3.0 3 90.0 0.8866 114985.5\n",
      "I am working on tract number 1500 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1500 2 597761.3683522958 3.7898\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1500 2 75590.5917259159 1.8949\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1500 2 711222.5842736469 4.1678\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1500 2 476125.28009306936 3.0313\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1500 2 158721.01585538546 2.4631\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1500 2 409622.12377796625 2.7472\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1500 2 291198.7095535771 2.6052\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1500 2 233853.78133820827 2.5341\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1500 2 199109.6054977793 2.4986\n",
      "loop31.0, tr1500,wedgePops761778.28, 224789.9, 94929.9, 216928.7, 225129.7, Overedge?0, 1, 0, 0, ,Satisfied?1001,yoyo?00124 \n",
      "   targetWP, latest drx4 are tWP,dr, 225198.9,0.002, 225198.9,1.3502, 225198.9,0.0178, 225198.9,0.0103\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1500 2 216928.716307248 2.5164\n",
      "loop32.0, tr1500,wedgePops770279.66, 224789.9, 94929.9, 225430.1, 225129.7, Overedge?0, 1, 0, 0, ,Satisfied?1001,yoyo?00124 \n",
      "   targetWP, latest drx4 are tWP,dr, 225198.9,0.002, 225198.9,1.3502, 225198.9,0.0089, 225198.9,0.0103\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1503 5.0 1 90.0 2.7123 438201.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1514 0 457675.0217648408 3.1501\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1514 0 45300.68303216592 1.575\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1514 0 457285.7421368803 3.1492\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1514 0 314759.5601655502 2.3621\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1514 0 73424.4127338817 1.9686\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1514 0 155234.3739755589 2.1654\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1514 0 232749.3063078404 2.2637\n",
      "I am working on tract number 1520 of 1784 tracts\n",
      "I am working on tract number 1540 of 1784 tracts\n",
      "I am working on tract number 1560 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1570 3 712757.8527164852 3.9994\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1570 1 589290.9649558445 3.5766\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1570 1 6419198.12754919 7.909\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1570 1 6327670.567879166 5.7428\n",
      "loop31.0, tr1570,wedgePops5917803.41, 62212.7, 5752995.1, 62114.4, 40481.2, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0800 \n",
      "   targetWP, latest drx4 are tWP,dr, 605620.0,1.44, 605620.0,-1.0831, 62212.7,-0.0016, 605620.0,-1.9997\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1570 1 5752995.145158634 4.6597\n",
      "loop32.0, tr1570,wedgePops4174578.94, 62212.7, 4009770.7, 62114.4, 40481.2, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0800 \n",
      "   targetWP, latest drx4 are tWP,dr, 605620.0,1.44, 605620.0,-0.5415, 62212.7,-0.0016, 605620.0,-1.9997\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1570 1 4009770.67791006 4.1181\n",
      "loop33.0, tr1570,wedgePops1802060.63, 62212.7, 1637252.4, 62114.4, 40481.2, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0800 \n",
      "   targetWP, latest drx4 are tWP,dr, 605620.0,1.44, 605620.0,-0.2708, 62212.7,-0.0016, 605620.0,-1.9997\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1570 1 1637252.3716002214 3.8474\n",
      "loop34.0, tr1570,wedgePops1061638.5, 62212.7, 896830.2, 62114.4, 40481.2, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0800 \n",
      "   targetWP, latest drx4 are tWP,dr, 605620.0,1.44, 605620.0,-0.1354, 62212.7,-0.0016, 605620.0,-1.9997\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1570 1 896830.2394483816 3.712\n",
      "loop35.0, tr1570,wedgePops902442.86, 62212.7, 737634.6, 62114.4, 40481.2, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0800 \n",
      "   targetWP, latest drx4 are tWP,dr, 605620.0,1.44, 605620.0,-0.0677, 62212.7,-0.0016, 605620.0,-1.9997\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1570 1 737634.5943459615 3.6443\n",
      "loop36.0, tr1570,wedgePops820150.33, 62212.7, 655342.1, 62114.4, 40481.2, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0800 \n",
      "   targetWP, latest drx4 are tWP,dr, 605620.0,1.44, 605620.0,-0.0338, 62212.7,-0.0016, 605620.0,-1.9997\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1570 1 655342.0627209946 3.6104\n",
      "loop37.0, tr1570,wedgePops783820.82, 62212.7, 619012.6, 62114.4, 40481.2, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0800 \n",
      "   targetWP, latest drx4 are tWP,dr, 605620.0,1.44, 605620.0,-0.0169, 62212.7,-0.0016, 605620.0,-1.9997\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1570 1 619012.5597759107 3.5935\n",
      "loop38.0, tr1570,wedgePops768187.12, 62212.7, 603378.9, 62114.4, 40481.2, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?0800 \n",
      "   targetWP, latest drx4 are tWP,dr, 605620.0,1.44, 605620.0,-0.0085, 62212.7,-0.0016, 605620.0,-1.9997\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1571 0 205385.92724071036 2.3275\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1571 0 11691.41977731479 1.1637\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1571 0 430019.3191282706 2.4652\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1571 0 29388.261356232513 1.8145\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1571 0 79204.83610630283 2.1398\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1571 0 153840.4824701759 2.3025\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1571 0 320414.7763900448 2.3839\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1571 0 242250.98778069828 2.3432\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1573 2 372058.11898553465 1.9892\n",
      "I am working on tract number 1580 of 1784 tracts\n",
      "I am working on tract number 1600 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1600 1 462307.265941564 3.4673\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1600 1 46573.82660944172 1.7337\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1600 1 478264.9049610842 3.6056\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1600 1 135520.5826316051 2.6696\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1600 1 269229.5910907521 3.1376\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1600 1 431634.4280522322 3.3716\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1600 1 329771.4118553332 3.2546\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1600 1 394398.97429804446 3.3131\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1600 1 366967.22496741265 3.2839\n",
      "14 yoyos for tract,wedge,wedgePop,r= 1600 1 347686.3621876105 3.2692\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1603 4.0 3 90.0 1.3994 396875.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1603 5.0 3 90.0 1.2889 396875.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1603 6.0 3 90.0 1.1871 396875.0\n",
      "we have 2 non-opposing shorted wedges for tract no 1607\n",
      "I am working on tract number 1620 of 1784 tracts\n",
      "I am working on tract number 1640 of 1784 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1641\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1648 3 492915.15733193216 3.328\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1648 0 206998.98121190871 1.3699\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1648 3 45637.17360633565 1.664\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1648 0 62211.013785130664 0.685\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1648 3 493963.9967195933 3.3331\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1648 0 208175.54746243168 1.4085\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1648 3 348829.59029205673 2.4986\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1648 0 171422.69774987927 1.0468\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1648 3 94843.01058416467 2.0813\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1648 0 178826.16754798943 1.2277\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1648 3 248935.64762227124 2.2899\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1648 0 196649.506803711 1.3181\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1648 3 176675.47127981271 2.1856\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1648 0 186054.00234505686 1.2729\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1648 3 224999.93570422733 2.2378\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1648 0 191136.94156634252 1.2955\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1648 3 203555.1339688752 2.2117\n",
      "I am working on tract number 1660 of 1784 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1660\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1677 3 497283.44246368675 3.3098\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1677 3 47588.51319119334 1.6549\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1677 3 499141.22430261626 3.3207\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1677 3 354108.1178048939 2.4878\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1677 3 83367.03973763908 2.0714\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1677 3 202231.27858698994 2.2796\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1677 3 120477.65690055555 2.1755\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1677 3 157988.8095551634 2.2275\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1677 3 179948.1786182187 2.2536\n",
      "I am working on tract number 1680 of 1784 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1692 4.0 1 90.0 0.6136 126547.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1692 5.0 1 90.0 0.8287 126547.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1692 6.0 1 90.0 1.1193 126547.9\n",
      "I am working on tract number 1700 of 1784 tracts\n",
      "I am working on tract number 1720 of 1784 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1733\n",
      "we have 2 non-opposing shorted wedges for tract no 1734\n",
      "we have 2 non-opposing shorted wedges for tract no 1735\n",
      "I am working on tract number 1740 of 1784 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1753\n",
      "we have 2 non-opposing shorted wedges for tract no 1754\n",
      "we have 2 non-opposing shorted wedges for tract no 1755\n",
      "we have 2 non-opposing shorted wedges for tract no 1758\n",
      "I am working on tract number 1760 of 1784 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1762 3 643464.8216527727 4.6563\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1762 3 65018.83208824904 2.3282\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1762 3 653968.0038860411 4.8933\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1762 3 235883.2019178935 3.6107\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1762 3 433369.2991724935 4.252\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1762 3 636309.4455936963 4.5726\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1762 3 576188.3408419916 4.4123\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1762 3 620880.5027340313 4.4925\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1762 3 607985.3644681533 4.4524\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1762 3 593513.8367920088 4.4324\n",
      "I am working on tract number 1780 of 1784 tracts\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.0000876106558814\n",
      "Average and max number of wedgePop loops per tract were:  8.917600896860986 40.0\n",
      "min,average,max of Home District areas were:  0.0 2.416038006944302 11.767229297201595\n",
      "calculated statewide vote was 0.4093769848519478, should have been 0.4007449681520931\n",
      "calcd statewide Hispanic pop was 0.11268872815679225, should have been 0.11463861454093315\n",
      "calcd statewide Black pop was 0.03562528428378101, should have been 0.03823417275148974\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.3845291479820628\n"
     ]
    }
   ],
   "source": [
    "#3/7 - 3/10/22 - linear wideAngle, short the opposing wedgePop to match first shorted wedge (see nonper toy)\n",
    "# Bisect for yoyo > 3, precinct calcs after wedges finalized.  More aggressive Euler gain\n",
    "#minTractPop = 10  #this is set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "# guessedR = [0.]*nWedges  #obsolete; was wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopTractUse = [0.] * nTracts  #same as above two, but only for the current loop / tract\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "toler = 0.005  #adjustable - fractional slop in district population.  normally 0.01, to be reduced to 0.005\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "nGiveUp = 40  #we punt after this many loops\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "EulerGain = 1.5 #if we hit empty land, gain more aggressively to get through it\n",
    "#yoyoFactor = 0.3 #rate of reduction in gain as solver continues to yoyo in solving a wedge  #not currently used ***\n",
    "globalMax_dr = (MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "maxAngleRatio = 1.9  #for tightening tract weighting near boundaries  #NEW 3/7/22\n",
    "levelL = 0.36 #sqrt(pop) for angle=std  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8 * pi  #adjustable parameter.  (avoid wedges too close to half a pie\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.  #running total of this tract's \"home district\" population\n",
    "    nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "    targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    isSatisfied = [0] * nWedges #wedges close enough to target are not improved upon\n",
    "    yoyoCount = [0] * nWedges #tracks how many times we've reversed this wedge's growth vs. shrink at current wPop target\n",
    "    wedgeMaxR = [0.] * nWedges #will track the largest radius this wedge has seen (to cap yoyo bisection step)\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "    currentR = [0.]*nWedges\n",
    "    old_dr = [0.]*nWedges\n",
    "    wedgeStop = 0  #obsolete? this will flag if we need to rejigger oldR when we stopped an over-boundary wedge\n",
    "    for nW in range(nWedges):\n",
    "        currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "        old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+angle0[t]   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ]) #build a tiny starter wedge triangle\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1):\n",
    "        #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        sumWedgePopGapChange = 0  #this will total wedge pops that fall short of expectations (when over boundary)\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            neededWedgePop = targetWedgePop[nW] - wedgePop[nW]\n",
    "            isSatisfied[nW] = 0  #default in case target changed last loop.  We'll check immediately below\n",
    "            if abs(neededWedgePop/targetWedgePop[nW]) < 0.5*toler :  #is this wedge close enough to stop iterating on it?\n",
    "                isSatisfied[nW] = 1\n",
    "            if isOverEdge[nW] == 0 and isSatisfied[nW] == 0:   #we skip over-boundary and near-perfect wedges\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]  #how much wedgePop gained in last loop\n",
    "                if (wedgePopDelta == 0): #our last wedge change was in an empty area (desert or off map)\n",
    "                    gainAdjust = EulerGain/gain  #Euler method often over-cautious at map edges or in sparse areas\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] * targetWedgePop[nW] / wedgePop[nW]  #Euler guess\n",
    "                    # can't use Newton-Raphson since last dy was zero, use Euler instead\n",
    "                    print(\"we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop\",t,tractLoopCounter[t],\n",
    "                          nW, round(180./pi*wedgeAngle[nW],1),round(currentR[nW],4),round(wedgePop[nW],1))\n",
    "                else: #use N-R estimation\n",
    "                    gainAdjust = 1.0\n",
    "                    R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop\n",
    "                    \n",
    "                guessedRsquared = max( guessedRsquared, 0. ) #minimum guardrail\n",
    "                guessed_dr = guessedRsquared ** 0.5 - currentR[nW]  #best guess for wedge radius change\n",
    "                #gain = max(0.3,normalGain ** (1. + yoyoFactor * yoyoCount[nW]))  #obsolete; using bisect\n",
    "                dr = gain * gainAdjust * guessed_dr   #gain typically ~0.8 for stability\n",
    "                dr = max( -0.5*currentR[nW],min(dr,max_dr[nW]) )  #apply min and max guardrails to wedge radius change\n",
    "                \n",
    "                if np.sign(dr) != np.sign(old_dr[nW]) :  #are we yoyoing? How many times?\n",
    "                    yoyoCount[nW] += 1\n",
    "                if yoyoCount[nW] > 3 and yoyoCount[nW] <= 6 :\n",
    "                    wedgeMaxR[nW] = max(wedgeMaxR[nW],currentR[nW]) #will track largest R in yoyo cycle\n",
    "                    max_dr[nW] = 2.*wedgeMaxR[nW]  #this will now store a reasonable max bisection step size\n",
    "                if yoyoCount[nW] > 6 :  #switch to bisection method; too many yo-yo's\n",
    "                    print(yoyoCount[nW],\"yoyos for tract,wedge,wedgePop,r=\",t,nW,wedgePop[nW],round(currentR[nW],4) )\n",
    "                    max_dr[nW] = 0.5 * max_dr[nW] #we are now bisecting ...\n",
    "                    if(neededWedgePop < 0):\n",
    "                        dr = max(-1.*max_dr[nW], -0.5*currentR[nW])  #reduce wedge size\n",
    "                    else:\n",
    "                        dr = max_dr[nW]  #increase wedge size\n",
    "                old_dr[nW] = dr  #these three lines -- save current as old values for next loop\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge trapezoid piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge trapezoid piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                    loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                        # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop[nW] : #if growing, confirm we're not beyond MAP boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])\n",
    "                    if leadingEdge.intersects(MAP) :  #still room to grow in part of edge, keep going\n",
    "                        gerrymandering = \"evil\"  #it had to be said\n",
    "                    else:  #this wedge is fully beyond the map. give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        oldWedgePopGap = wedgePopGap[nW]\n",
    "                        wedgePopGap[nW] = targetWedgePop[nW] - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1  #a few lines below, we will adjust other wedge's targets\n",
    "                        sumWedgePopGapChange += wedgePopGap[nW] - oldWedgePopGap\n",
    "                        if (nActiveWedges == 0) : #we're doomed, somehow all wedges are short and off map\n",
    "                            print(\"PUNT! no more active wedges for tract, loop =\",t,tractLoopCounter[t] )\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "                            \n",
    "                        max_dr = [globalMax_dr]*nWedges   #allow other wedges to take big steps again to catch up\n",
    "                        yoyoCount = [0] * nWedges  #go back to original gains on ALL active wedges\n",
    "                   \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop.\n",
    "        nReceivingWedges = nActiveWedges\n",
    "        oppFlag = 0\n",
    "        if targetWedgePop[int(nWedges/2)] < 0.99 * avgDistrictPop/float(nWedges): #is opposite wedge restricted?\n",
    "            oppFlag = 1\n",
    "            nReceivingWedges -= 1-isOverEdge[int(nWedges/2)]  #if yes, don't count opposite wedge as a receiver\n",
    "        if nReceivingWedges == 0: #rarely, the opposite wedge is the only active one\n",
    "            for nWW in range(nWedges): #in this case, we allow the opposite wedge to pick up the slack\n",
    "                targetWedgePop[nWW] += sumWedgePopGapChange/max(1.,float(nReceivingWedges) )\n",
    "        else: #if the opp wedge has a ceiling wedgePopTarget, distribute wedgePopGap to other active wedges\n",
    "            for nWW in range(nWedges):  #time to make adjustments in target wedge pops based on boundary fails\n",
    "                if nWW != int(nWedges/2) or oppFlag == 0 :  #excluding opposite wedge as a receiver\n",
    "                    targetWedgePop[nWW] += sumWedgePopGapChange/float(nReceivingWedges)\n",
    "\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #to ensure we adjust wedge angles and target wedgePops at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                oppW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the last shorted wedge\n",
    "                if (isOverEdge[oppW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges via a complex weighting\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            oppW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[oppW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    #wedgeAngle=[avgWedgeAngle]*nWedges  #comment this out, no change\n",
    "            else: # a single shorted wedge (over boundary).  COMPLETELY RESTART wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point               \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation, normal angle   # ****************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0. #the wedgePop of the shorted wedge if we re-orient but don't adjust angles\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                #printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                #now, we \"squish the square\" based on how close we are to boundary - see offline documentation\n",
    "                equivL = (4.*minWedgePop[t] / avgDistrictPop)**0.5  #non-dimensional centroid-boundary distance\n",
    "                wideAngle = avgWedgeAngle*max(  1,( 1.+(maxAngleRatio-1.)*(levelL - equivL)/(levelL - 0.) )  )\n",
    "                wideAngle = min(maxWideAngle, wideAngle)  #prevent too wide of an angle (half-pie)\n",
    "                #HERE, WE ADJUST all wedge ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit to avoid gaping wedges\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #** COMPLETELY RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                nActiveWedges = nWedges\n",
    "                # NEW 3/10/22 - we not only squish the angles, we short the target for the wedge opposite boundary\n",
    "                oppWedgeExpectedPop = minWedgePop[t] * math.tan(0.5*wideAngle)/math.tan(pi/nWedges)  #prediction for new wedge0's pop\n",
    "                oppWedgeExpectedPop = min(oppWedgeExpectedPop,avgDistrictPop/nWedges) #not needed, but just in case...\n",
    "                for nW in range(nWedges):\n",
    "                    if nW == int(nWedges/2):\n",
    "                        targetWedgePop[nW] = oppWedgeExpectedPop\n",
    "                    else:  #NOTE: we don't mess w wedge0's target.  When it shorts again, we will adjust other wedge's tgts\n",
    "                        targetWedgePop[nW] =(avgDistrictPop - oppWedgeExpectedPop)/float(nWedges-1)\n",
    "                # *** END OF 3/10/22 MODIFICATION\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = [tinyR]*nWedges  #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                isSatisfied = [0] *nWedges\n",
    "                yoyoCount = [0] *nWedges #just in case we missed this earlier\n",
    "                wedgeMaxR = [0.] * nWedges #resetting\n",
    "                angle[0] = angle0[t] - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = angle0[t] - 0.5*wideAngle  #we have to start somewhere :-)\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                HDpop = np.sum(wedgePop)   #** END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if (tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,    #and (don't) reduce gain\n",
    "            strPop = str(round(HDpop,2))\n",
    "            strWdg = \"Overedge?\"\n",
    "            strSat = \"Satisfied?\"\n",
    "            strYoyo = \"yoyo?\"                \n",
    "            strTWP_dr = \"tWP,dr\"\n",
    "            for nWW in range(nWedges) :\n",
    "                strPop = strPop + \", \"+str(round(wedgePop[nWW],1) )\n",
    "                strWdg = strWdg +str(isOverEdge[nWW])+\", \"\n",
    "                strSat = strSat +str(isSatisfied[nWW])\n",
    "                strYoyo = strYoyo + str(yoyoCount[nWW])\n",
    "                strTWP_dr = strTWP_dr + \", \"+str(round(targetWedgePop[nWW],1) )+\",\"+str(round(old_dr[nWW],4) )\n",
    "            print(\"loop{0}, tr{1},wedgePops{2}, {3},{4},{5} \".format(tractLoopCounter[t],t,strPop,strWdg,strSat,strYoyo) )\n",
    "            print(\"   targetWP, latest drx4 are\",strTWP_dr)\n",
    "\n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,oldWedgePop, wedgePop, overEdge?\",nWW,str(round(currentR[nWW],5)), \n",
    "                      str(round(oldWedgePop[nWW],4)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    totGOP = 0.\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  #zero these out prior to summing over final wedges\n",
    "    usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "    usedPrecinct = [0]*nPrecincts\n",
    "\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1): \n",
    "        #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        # HDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        # HDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+currentR[0]*math.cos(angle[0]), tractCP.y+currentR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+currentR[0]*math.cos(angle2[0]),tractCP.y+currentR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(nWedges):\n",
    "            HDradius[t][nW] = currentR[nW]\n",
    "            HDangle[t][nW] = angle[nW]\n",
    "            \n",
    "        for nW in range(1,nWedges):  #save final geometry, compute minority and partisan stats\n",
    "            cR = currentR[nW] #shorthand\n",
    "            outerPt2 = Point(tractCP.x+cR*math.cos(angle[nW]), tractCP.y+cR*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+cR*math.cos(angle2[nW]),tractCP.y+cR*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area  #for stats, final Home District area\n",
    "        for nG in range(nGrids) :  # ID the ACTIVE grids to look for intersecting precincts\n",
    "            gridIntersxn = gridGeom[nG].intersection(HDpolly)\n",
    "            if (gridIntersxn.area > 0) :  #this grid is RELEVANT to the final Home District\n",
    "                for tt in range(nGridTracts[nG]):\n",
    "                    nTT = gridTractNo[nG][tt]\n",
    "                    if usedTract[nTT] == 0: #only examine tracts that have not already been called for intersection\n",
    "                        usedTract[nTT] == 1  #this tract has now been called \n",
    "                        overlap = tractGeom[nTT].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/tractArea[nTT]\n",
    "                            totVAP += fracArea*tractVAP[nTT]\n",
    "                            totHisp += fracArea*tractHisp[nTT]\n",
    "                            totBlack += fracArea*tractBlack[nTT]\n",
    "                        \n",
    "                for pp in range(nGridPrecincts[nG]): #scanning the list of precincts in this active grid\n",
    "                    nPP = gridPrecinctNo[nG][pp]\n",
    "                    if usedPrecinct[nPP] == 0  :\n",
    "                        usedPrecinct[nPP] = 1  #don't double up on precinct intersection\n",
    "                        overlap = vtdGeom[nPP].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/vtdArea[nPP]\n",
    "                            totGOP += fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                            totVote += fracArea*vtdPop[nPP]\n",
    "                            loopPrecinctUse[nPP] += overlap/vtdArea[nPP] *tractPop[t]/avgDistrictPop\n",
    "        HDvHisp[t] = totHisp/totVAP\n",
    "        HDvBlack[t] = totBlack/totVAP\n",
    "        HDvGOP[t] = totGOP / totVote\n",
    "        for nPP in range (nPrecincts) :\n",
    "            precinctUse[nPP] += loopPrecinctUse[nPP] #add to global use for this precinct\n",
    "            \n",
    "        \n",
    "\n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "24a347a9-8220-4c1a-bcfc-14923f1f98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of weights over all precinct home districs should have been 1.000, but was  1.0000876106558814\n",
      "Average and max number of wedgePop loops per tract were:  8.917600896860986 40.0\n",
      "min,average,max of Home District areas were:  0.0 2.416038006944302 11.767229297201595 for  WA\n"
     ]
    }
   ],
   "source": [
    "#use this line to document progress\n",
    "# start WA 5:42, end 7:06\n",
    "print(\"Sum of weights over all precinct home districs should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea),\"for \",STATE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WA 18Mar\n"
     ]
    }
   ],
   "source": [
    "date = \"18Mar\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "c55ac5fd-7402-47b9-9093-10b9fe0912b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"maxWideAngle\",\"levelL\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, maxWideAngle, levelL]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD1tol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "c4449da0-47a9-40cc-a585-8288447290e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "487 1.02 ( -121.49725858544075 45.728989500307584 ) 0.5137083620486564 t, pop/target,(x,y), pctR\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will redo tract numbers  [0]\n"
     ]
    }
   ],
   "source": [
    "# Looking for nonconvergers ...  Run even if none observed, just to check  \n",
    "redo = [0]\n",
    "counter = 0.\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if (ratio < 0.9 or ratio > 1.1) :  #these we will redo\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=14)\n",
    "            if counter == 0 :  #first redo\n",
    "                redo = [t]\n",
    "            else :\n",
    "                redo.append(t)\n",
    "            counter+= 1\n",
    "        else: #show these milder offenders on the map in a smaller font            \n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()\n",
    "print(\"we will redo tract numbers \",redo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "be6e965b-4ca8-4af2-8031-63132fbb939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.7722347107213436 756 pop,GOP 0.5661375661375662\n",
      "7 0.7933126285249027 874 pop,GOP 0.522883295194508\n",
      "64 0.7998779528167683 840 pop,GOP 0.6023809523809524\n",
      "124 0.7520387890536959 586 pop,GOP 0.5853242320819113\n",
      "133 0.7986661285040149 971 pop,GOP 0.4840370751802266\n",
      "135 0.7676710862269636 642 pop,GOP 0.6869158878504673\n",
      "137 0.7951834295342788 446 pop,GOP 0.57847533632287\n",
      "178 0.7596336648998328 408 pop,GOP 0.6691176470588235\n",
      "272 0.7251093401968761 251 pop,GOP 0.42231075697211157\n",
      "281 0.7713673502057602 5464 pop,GOP 0.3709736456808199\n",
      "287 0.7822993734017439 6591 pop,GOP 0.3782430587164315\n",
      "292 0.7387008320334674 890 pop,GOP 0.7235955056179775\n",
      "293 0.7767860785255754 6147 pop,GOP 0.38848218643240606\n",
      "305 0.7841053271577902 5778 pop,GOP 0.3771201107649706\n",
      "306 0.7365994440280953 902 pop,GOP 0.417960088691796\n",
      "796 0.7696868381896006 337 pop,GOP 0.7062314540059347\n",
      "801 0.7196551436966517 593 pop,GOP 0.7268128161888702\n",
      "807 0.7487278534642344 212 pop,GOP 0.4669811320754717\n",
      "814 0.7488468165427521 135 pop,GOP 0.5185185185185185\n",
      "818 0.7679846001018841 816 pop,GOP 0.5674019607843137\n",
      "820 0.7494355199730398 452 pop,GOP 0.45132743362831856\n",
      "821 0.7946569209720301 102 pop,GOP 0.7549019607843137\n",
      "824 0.7913231040963904 213 pop,GOP 0.7793427230046949\n",
      "826 0.7214180536123296 270 pop,GOP 0.7074074074074074\n",
      "827 0.7314868749761638 973 pop,GOP 0.762589928057554\n",
      "829 0.7382953147085107 782 pop,GOP 0.7250639386189258\n",
      "834 0.6807375915061269 318 pop,GOP 0.7547169811320755\n",
      "876 0.7906533011367846 1010 pop,GOP 0.7029702970297029\n",
      "896 0.7763393256278492 624 pop,GOP 0.7243589743589743\n",
      "901 0.7634420958525606 884 pop,GOP 0.751131221719457\n",
      "903 0.7902154627121243 538 pop,GOP 0.6338289962825279\n",
      "905 0.7355117151510316 671 pop,GOP 0.6304023845007451\n",
      "906 0.7164166125824576 723 pop,GOP 0.5629322268326418\n",
      "907 0.781879432045501 666 pop,GOP 0.4174174174174174\n",
      "916 0.7032110322045247 1007 pop,GOP 0.5402184707050646\n",
      "919 0.7581300388161732 555 pop,GOP 0.581981981981982\n",
      "925 0.784486816162488 632 pop,GOP 0.40664556962025317\n",
      "926 0.7762202300625367 566 pop,GOP 0.5865724381625441\n",
      "930 0.7490816805915003 743 pop,GOP 0.42799461641991926\n",
      "931 0.7022717322832516 695 pop,GOP 0.6517985611510791\n",
      "966 0.7332488916211491 1385 pop,GOP 0.6563176895306859\n",
      "967 0.7493101442614913 580 pop,GOP 0.6448275862068965\n",
      "968 0.7668181289762097 624 pop,GOP 0.625\n",
      "971 0.7727002983908768 566 pop,GOP 0.5318021201413428\n",
      "972 0.6789853293831065 1081 pop,GOP 0.5254394079555966\n",
      "973 0.7936515890588333 947 pop,GOP 0.6029567053854277\n",
      "977 0.6510411267624362 782 pop,GOP 0.59846547314578\n",
      "979 0.6983130994290943 1226 pop,GOP 0.6443719412724307\n",
      "982 0.6691033942893785 751 pop,GOP 0.6458055925432756\n",
      "984 0.688853411480437 954 pop,GOP 0.6037735849056604\n",
      "986 0.6856600541139412 490 pop,GOP 0.5204081632653061\n",
      "987 0.7470912943684349 1176 pop,GOP 0.6045918367346939\n",
      "988 0.671639216390136 1012 pop,GOP 0.575098814229249\n",
      "989 0.6868861848814237 1004 pop,GOP 0.5029880478087649\n",
      "990 0.7836945723542482 1028 pop,GOP 0.580739299610895\n",
      "991 0.7963665419776654 789 pop,GOP 0.5944233206590621\n",
      "993 0.6845044073194468 904 pop,GOP 0.6017699115044248\n",
      "994 0.7780215432977992 663 pop,GOP 0.5927601809954751\n",
      "995 0.6949127717658967 1142 pop,GOP 0.500875656742557\n",
      "1003 0.7307354127881952 714 pop,GOP 0.5896358543417367\n",
      "1006 0.7588464699213467 842 pop,GOP 0.5902612826603325\n",
      "1007 0.7473024340814992 1139 pop,GOP 0.6277436347673397\n",
      "1008 0.7479386122087176 895 pop,GOP 0.5217877094972067\n",
      "1010 0.7666428894996496 1173 pop,GOP 0.6445012787723785\n",
      "1012 0.7211047286847326 1174 pop,GOP 0.5979557069846678\n",
      "1013 0.7642534980528196 1139 pop,GOP 0.611062335381914\n",
      "1014 0.7730674963123314 1025 pop,GOP 0.5707317073170731\n",
      "1015 0.761660398997528 1157 pop,GOP 0.5929127052722558\n",
      "1017 0.7062080456389278 679 pop,GOP 0.6362297496318114\n",
      "1018 0.7662553882559795 921 pop,GOP 0.5689467969598263\n",
      "1020 0.7644768931811257 1020 pop,GOP 0.6107843137254902\n",
      "1021 0.793274903370531 505 pop,GOP 0.5346534653465347\n",
      "1022 0.787877608307422 735 pop,GOP 0.5836734693877551\n",
      "1025 0.7390663823592465 832 pop,GOP 0.5853365384615384\n",
      "1026 0.7725256445043448 744 pop,GOP 0.5228494623655914\n",
      "1027 0.7160005948663837 775 pop,GOP 0.6141935483870967\n",
      "1028 0.7374029704848709 881 pop,GOP 0.5720771850170261\n",
      "1032 0.7307055479632255 903 pop,GOP 0.717607973421927\n",
      "1036 0.7622179170396716 5827 pop,GOP 0.3712030204221726\n",
      "1048 0.6807115283490967 651 pop,GOP 0.674347158218126\n",
      "1053 0.7795707887011414 5698 pop,GOP 0.3681993681993682\n",
      "1061 0.6903662415625313 768 pop,GOP 0.6236979166666666\n",
      "1064 0.69079452369081 692 pop,GOP 0.7182080924855492\n",
      "1068 0.6475020407495952 822 pop,GOP 0.681265206812652\n",
      "1070 0.6700855073048312 933 pop,GOP 0.6881028938906752\n",
      "1072 0.7910396505129957 789 pop,GOP 0.688212927756654\n",
      "1074 0.6881767647677592 687 pop,GOP 0.6477438136826783\n",
      "1075 0.6464670895373298 944 pop,GOP 0.684322033898305\n",
      "1078 0.7075767056268815 699 pop,GOP 0.6881258941344778\n",
      "1079 0.784689153030593 622 pop,GOP 0.7009646302250804\n",
      "1080 0.7601055716484577 782 pop,GOP 0.6099744245524297\n",
      "1081 0.6746086043781301 792 pop,GOP 0.571969696969697\n",
      "1082 0.7064190387111797 568 pop,GOP 0.6338028169014085\n",
      "1083 0.6614742984728961 791 pop,GOP 0.7269279393173198\n",
      "1085 0.7338371468314074 1082 pop,GOP 0.6700554528650647\n",
      "1086 0.6932478127385405 612 pop,GOP 0.6977124183006536\n",
      "1093 0.7706489820937825 548 pop,GOP 0.3740875912408759\n",
      "1101 0.7912348278470029 5499 pop,GOP 0.36933987997817785\n",
      "1122 0.7789437696080932 32 pop,GOP 0.5\n",
      "1127 0.7900294298411951 932 pop,GOP 0.5611587982832618\n",
      "1129 0.757117887292284 1268 pop,GOP 0.4195583596214511\n",
      "1143 0.7887794257104535 711 pop,GOP 0.42334739803094235\n",
      "1174 0.13764257644593214 49 pop,GOP 0.6122448979591837\n",
      "1198 0.7419467797205941 526 pop,GOP 0.5798479087452472\n",
      "1201 0.7352403535236813 6226 pop,GOP 0.39431416639897204\n",
      "1202 0.7420534515783266 1215 pop,GOP 0.5876543209876544\n",
      "1209 0.780034130605504 6462 pop,GOP 0.40436397400185703\n",
      "1216 0.7440204135949294 419 pop,GOP 0.5083532219570406\n",
      "1317 0.7972315586362244 1132 pop,GOP 0.36837455830388693\n",
      "1370 0.6667131933422864 161 pop,GOP 0.6521739130434783\n",
      "1817 0.7994433777164746 1108 pop,GOP 0.29693140794223827\n",
      "2008 0.7364963587750211 1093 pop,GOP 0.4464775846294602\n",
      "2375 0.7569945559739464 470 pop,GOP 0.5659574468085107\n",
      "2624 0.7762648316580044 6504 pop,GOP 0.392220172201722\n",
      "3015 0.7816624490617098 694 pop,GOP 0.553314121037464\n",
      "3104 0.7919962991760168 219 pop,GOP 0.5251141552511416\n",
      "3110 0.7960395422402782 628 pop,GOP 0.4601910828025478\n",
      "3120 0.7608254862444501 724 pop,GOP 0.462707182320442\n",
      "3125 0.7736716997796151 575 pop,GOP 0.4852173913043478\n",
      "3131 0.7915327748541818 244 pop,GOP 0.6065573770491803\n",
      "3133 0.7868084487491466 666 pop,GOP 0.47897897897897895\n",
      "3156 0.7836873381569341 981 pop,GOP 0.5229357798165137\n",
      "3166 0.7712368310084943 552 pop,GOP 0.4673913043478261\n",
      "3170 0.7558593540006812 913 pop,GOP 0.44687842278203727\n",
      "3176 0.774229175195624 724 pop,GOP 0.5290055248618785\n",
      "3195 0.766433252906385 892 pop,GOP 0.46524663677130046\n",
      "3198 0.7697446627536464 934 pop,GOP 0.4014989293361884\n",
      "3840 0.7778486674592634 261 pop,GOP 0.5977011494252874\n",
      "3982 0.7907741730091145 83 pop,GOP 0.4939759036144578\n",
      "3985 0.7814742531328347 497 pop,GOP 0.5372233400402414\n",
      "3992 0.7755747016966689 533 pop,GOP 0.5684803001876173\n",
      "3996 0.7922146435645515 19 pop,GOP 0.3684210526315789\n",
      "4063 0.7931969181330195 1103 pop,GOP 0.614687216681777\n",
      "4068 0.7922686878866787 1002 pop,GOP 0.6497005988023952\n",
      "4225 0.7549484616743852 1041 pop,GOP 0.5456292026897214\n",
      "4231 0.7793579700584806 1189 pop,GOP 0.5449957947855341\n",
      "4236 0.7777864003377035 1 pop,GOP 1.0\n",
      "4341 0.7472922699463138 31 pop,GOP 0.41935483870967744\n",
      "5060 0.7418588428003839 908 pop,GOP 0.5605726872246696\n",
      "5068 0.7429270403573075 817 pop,GOP 0.4614443084455324\n",
      "5079 0.7681247987702561 474 pop,GOP 0.5316455696202531\n",
      "5080 0.7613873288284918 1402 pop,GOP 0.5592011412268189\n",
      "5091 0.7398853660722315 1048 pop,GOP 0.5467557251908397\n",
      "5092 0.7500479238155495 1067 pop,GOP 0.6579194001874414\n",
      "5098 0.7343897549559236 1259 pop,GOP 0.5631453534551231\n",
      "5135 0.7103343372466644 227 pop,GOP 0.6784140969162996\n",
      "5136 0.727349416941762 370 pop,GOP 0.6243243243243243\n",
      "5138 0.734881289036727 404 pop,GOP 0.693069306930693\n",
      "5139 0.7564133344105186 314 pop,GOP 0.7898089171974523\n",
      "5146 0.7436805868012617 223 pop,GOP 0.7937219730941704\n",
      "5149 0.7813935567413497 207 pop,GOP 0.48792270531400966\n",
      "5150 0.7620530244174283 110 pop,GOP 0.7090909090909091\n",
      "5153 0.747031635391226 627 pop,GOP 0.6060606060606061\n",
      "5157 0.7218328386927524 290 pop,GOP 0.6482758620689655\n",
      "5158 0.7105385027537215 612 pop,GOP 0.6503267973856209\n",
      "5162 0.769868680255684 365 pop,GOP 0.6767123287671233\n",
      "5165 0.7511926201536491 1163 pop,GOP 0.4660361134995701\n",
      "5166 0.7798164410801256 9 pop,GOP 0.3333333333333333\n",
      "5173 0.7724307772579972 962 pop,GOP 0.6185031185031185\n",
      "5174 0.7929171011103558 1161 pop,GOP 0.6184323858742463\n",
      "5176 0.7754882458151877 398 pop,GOP 0.6733668341708543\n",
      "5177 0.7790163766337463 1146 pop,GOP 0.5698080279232112\n",
      "5178 0.7535919022441152 1085 pop,GOP 0.4525345622119816\n",
      "5179 0.7546907920205386 115 pop,GOP 0.6347826086956522\n",
      "5181 0.7701607545251808 1131 pop,GOP 0.6825817860300619\n",
      "5182 0.784433716366714 219 pop,GOP 0.6894977168949772\n",
      "5185 0.7902350061979387 900 pop,GOP 0.6855555555555556\n",
      "5186 0.7896130137905827 1186 pop,GOP 0.6821247892074199\n",
      "5187 0.7454280465600888 1078 pop,GOP 0.461038961038961\n",
      "5188 0.7771443659187103 800 pop,GOP 0.47375\n",
      "5189 0.7588926009627871 976 pop,GOP 0.5594262295081968\n",
      "5190 0.7810623710154263 668 pop,GOP 0.4745508982035928\n",
      "5191 0.7488416326303692 390 pop,GOP 0.6948717948717948\n",
      "5193 0.7621467538978359 879 pop,GOP 0.3833902161547213\n",
      "5194 0.7427600217672402 634 pop,GOP 0.5536277602523659\n",
      "5212 0.7702266008677643 552 pop,GOP 0.6920289855072463\n",
      "5223 0.7514342082445966 457 pop,GOP 0.47702407002188185\n",
      "5224 0.74264923305992 1111 pop,GOP 0.46354635463546356\n",
      "5287 0.7745274382650501 1007 pop,GOP 0.5114200595829196\n",
      "5289 0.7608773214778913 1164 pop,GOP 0.6005154639175257\n",
      "5290 0.7965269925490079 729 pop,GOP 0.5925925925925926\n",
      "5296 0.7973320312797788 880 pop,GOP 0.6045454545454545\n",
      "5335 0.7750401599084615 413 pop,GOP 0.6222760290556901\n",
      "5349 0.7789001242701902 939 pop,GOP 0.5516506922257721\n",
      "5731 0.7788215541044776 6224 pop,GOP 0.38865681233933164\n",
      "5732 0.7405896350954142 795 pop,GOP 0.5383647798742138\n",
      "5744 0.7579498109336726 248 pop,GOP 0.3870967741935484\n",
      "5746 0.7838960969078739 745 pop,GOP 0.32348993288590605\n",
      "5752 0.777778278150139 935 pop,GOP 0.40855614973262033\n",
      "5756 0.7440420878046238 1017 pop,GOP 0.40904621435594885\n",
      "5758 0.7421092691166619 542 pop,GOP 0.44280442804428044\n",
      "5762 0.753157684859488 5 pop,GOP 0.6\n",
      "5763 0.7794668715666166 734 pop,GOP 0.3405994550408719\n",
      "5764 0.7893346599546198 759 pop,GOP 0.2753623188405797\n",
      "5766 0.7724830755820092 483 pop,GOP 0.36853002070393376\n",
      "5767 0.7593897210574806 567 pop,GOP 0.2980599647266314\n",
      "5769 0.7259478049300976 2 pop,GOP 0.0\n",
      "5777 0.7625091388380483 5964 pop,GOP 0.35429242119382964\n",
      "5790 0.7692156360896304 5915 pop,GOP 0.35536770921386307\n",
      "5795 0.7732766340861763 6091 pop,GOP 0.3473978000328353\n",
      "5799 0.7874012620240021 6241 pop,GOP 0.34834161192116647\n",
      "5800 0.7690170065565569 6242 pop,GOP 0.3425184235821852\n",
      "5802 0.7603741006072021 595 pop,GOP 0.2907563025210084\n",
      "5851 0.7835782085680492 536 pop,GOP 0.40298507462686567\n",
      "5852 0.7939037961430963 347 pop,GOP 0.37752161383285304\n",
      "5853 0.7696262572524306 861 pop,GOP 0.3530778164924506\n",
      "5854 0.7951413933450383 554 pop,GOP 0.2924187725631769\n",
      "5864 0.7574301442769616 1021 pop,GOP 0.3584720861900098\n",
      "5865 0.74697276819974 801 pop,GOP 0.36704119850187267\n",
      "5866 0.7951932053238263 664 pop,GOP 0.3057228915662651\n",
      "5867 0.7854320847688427 671 pop,GOP 0.17436661698956782\n",
      "5868 0.7695875290416121 655 pop,GOP 0.29923664122137406\n",
      "5869 0.7996354769626979 651 pop,GOP 0.25960061443932414\n",
      "5880 0.7911666203382457 919 pop,GOP 0.25680087051142547\n",
      "5881 0.7943150496791461 801 pop,GOP 0.23345817727840198\n",
      "5894 0.7789923704769847 733 pop,GOP 0.24556616643929058\n",
      "5897 0.7955779298369162 693 pop,GOP 0.20202020202020202\n",
      "5898 0.773901224815881 79 pop,GOP 0.25316455696202533\n",
      "5899 0.7666709770178298 955 pop,GOP 0.29214659685863875\n",
      "5911 0.7969554848024556 594 pop,GOP 0.12457912457912458\n",
      "5915 0.7960329415375446 351 pop,GOP 0.13105413105413105\n",
      "5916 0.7577278170466076 798 pop,GOP 0.33709273182957394\n",
      "5926 0.7950828309891423 692 pop,GOP 0.2239884393063584\n",
      "5928 0.7815321229235999 789 pop,GOP 0.3181242078580482\n",
      "5942 0.7721918204989177 530 pop,GOP 0.30377358490566037\n",
      "5945 0.7697091972278846 149 pop,GOP 0.053691275167785234\n",
      "5948 0.7785735066 1035 pop,GOP 0.5632850241545894\n",
      "5962 0.7897838685626251 582 pop,GOP 0.46048109965635736\n",
      "5963 0.7388140722974944 559 pop,GOP 0.4275491949910555\n",
      "5968 0.7746933603493706 901 pop,GOP 0.28967813540510545\n",
      "5969 0.7666669863533458 950 pop,GOP 0.31473684210526315\n",
      "5980 0.7416724482106936 257 pop,GOP 0.48249027237354086\n",
      "5987 0.7878611848053005 2 pop,GOP 0.5\n",
      "5990 0.7473780768380618 236 pop,GOP 0.4788135593220339\n",
      "5993 0.7652833829558799 370 pop,GOP 0.4972972972972973\n",
      "6001 0.7637643811013615 320 pop,GOP 0.4875\n",
      "6008 0.7788977069383659 483 pop,GOP 0.453416149068323\n",
      "6024 0.7661255419176698 35 pop,GOP 0.5142857142857142\n",
      "6046 0.779818574971583 328 pop,GOP 0.25914634146341464\n",
      "6051 0.7491562675694164 28 pop,GOP 0.6071428571428571\n",
      "6106 0.760660401232808 275 pop,GOP 0.2545454545454545\n",
      "6398 0.7462764525775463 705 pop,GOP 0.6425531914893617\n",
      "6399 0.7841306157376203 1058 pop,GOP 0.5907372400756143\n",
      "6439 0.7358140868625032 847 pop,GOP 0.7225501770956316\n",
      "6440 0.778379077465592 1160 pop,GOP 0.6974137931034483\n",
      "6938 0.7493095595446905 871 pop,GOP 0.49598163030998854\n",
      "6956 0.6784996355114734 990 pop,GOP 0.44646464646464645\n",
      "7005 0.7961910271991308 773 pop,GOP 0.38033635187580855\n",
      "7008 0.7821566769879117 635 pop,GOP 0.4582677165354331\n",
      "7271 0.7991174552874583 576 pop,GOP 0.5381944444444444\n",
      "7279 0.7908018915419615 892 pop,GOP 0.5168161434977578\n",
      "7282 0.78871324782798 716 pop,GOP 0.4860335195530726\n",
      "7299 0.739453135702633 14 pop,GOP 0.42857142857142855\n",
      "7302 0.797486154592083 5 pop,GOP 0.8\n",
      "7326 0.7599862167849973 811 pop,GOP 0.6498150431565968\n",
      "7341 0.786954821775539 5293 pop,GOP 0.36463253353485736\n",
      "7344 0.7575104036284929 427 pop,GOP 0.5667447306791569\n",
      "7347 0.76980671213946 5662 pop,GOP 0.3767220063581773\n",
      "7350 0.7775212155815708 6186 pop,GOP 0.3803750404138377\n",
      "7359 0.7823329434504798 6208 pop,GOP 0.38353737113402064\n",
      "7363 0.7748949566741321 1092 pop,GOP 0.6382783882783882\n",
      "7368 0.7580834341029986 1249 pop,GOP 0.5228182546036829\n",
      "7369 0.767822572694302 888 pop,GOP 0.5472972972972973\n",
      "7377 0.7834741220568651 934 pop,GOP 0.43147751605995716\n",
      "7378 0.7277957280355295 658 pop,GOP 0.49240121580547114\n",
      "7379 0.7695569671505632 421 pop,GOP 0.40617577197149646\n",
      "7381 0.748472198196388 1310 pop,GOP 0.4847328244274809\n",
      "7391 0.6638042125797857 15 pop,GOP 0.6\n",
      "7392 0.71701353078376 480 pop,GOP 0.49166666666666664\n",
      "7393 0.7049561727213494 387 pop,GOP 0.5116279069767442\n",
      "7394 0.6824000530686757 116 pop,GOP 0.4396551724137931\n",
      "7395 0.6759415758111741 611 pop,GOP 0.41734860883797054\n",
      "7396 0.7289121451309314 57 pop,GOP 0.6491228070175439\n",
      "7397 0.6886725517207712 605 pop,GOP 0.41487603305785126\n",
      "7398 0.7112575229276251 281 pop,GOP 0.4875444839857651\n",
      "7399 0.7063231959348749 959 pop,GOP 0.4337851929092805\n",
      "7445 0.738777665986203 788 pop,GOP 0.4593908629441624\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.8 and isSkippedPrecinct[p] == 0 and vtdPop[p] > 0: #ignore suppressed precincts\n",
    "        print(p,precinctUse[p], vtdPop[p],\"pop,GOP\",vtdGOP[p])\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[p].centroid.x\n",
    "        tractY = vtdGeom[p].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,pU, fontsize=9)\n",
    "        if notPolyVTD[p] == 0 :\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        else :\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "37f47a77-d58e-4b4c-be60-676819128e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (OPTIONAL) IF WE STOPPED AND RESTARTED, read the data back in:  #NEED TO UPDATE THIS W/ANGLES, RADII\n",
    "import pandas as pd\n",
    "infilename = \"FL_nD28tol0.01nW4.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDvPop = df2[\"HD-pop\"]\n",
    "HDvHisp = df2[\"HDvHisp\"]\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractPop = df2['tractPop']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "# nearEdge = df2['nearEdge']   #add this back in, PLEASE\n",
    "df2.head()\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9b2c0c0-fb53-44e5-9155-1963d4833ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "750596.7418101031 0.1131774380110593 0.0001361943772 0.0400616118781349 0.7450137754374908\n"
     ]
    }
   ],
   "source": [
    "#  HERE'S A BLOCK TO OVERWRITE THE REDONE DATA\n",
    "for r in range(nRedos):\n",
    "    t = redo[r]\n",
    "    HDvPop[t] = redoHDvPop[r]\n",
    "    HDvGOP[t] = redoHDvGOP[r]\n",
    "    tractArea[t] = redoHDarea[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c80e0880-6811-4408-b847-6d70bd2546ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt   #SKIP THIS BLOCK IF STARTING FROM FIRST BLOCK\n",
    "from scipy.stats import norm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all done with redo's.  Let's look at some outputs.  First, red/blue by home district  SHOULD BE MIX OF RED AND BLUE\n",
    "nPlot = 5\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "a5d3b510-6d1e-49ae-9a5d-03352e2b449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map of tracts that were used less than 0.7 of expectation in WA\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, where are those UNDERPERFORMING tracts (<0.x usage)\n",
    "maxPlot = 0.70\n",
    "print(\"map of tracts that were used less than {0} of expectation in {1}\".format(maxPlot, STATE) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy   #turn these on if I pull map back in\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "876011e8-d37e-44a6-8dd9-a1e4d2f51e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.3 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the OVERUSED tracts?\n",
    "minPlot = 1.3\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "19bfa7c8-0449-4182-8ed9-f5a1c1276f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of 16 tracts that were used more than 1.6 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the REALLY OVERUSED tracts?\n",
    "minPlot = 1.6\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "        counter +=1\n",
    "\n",
    "print(\"here is a map of {0} tracts that were used more than {1} of expectation\".format(counter, minPlot) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "f3e9bde3-06f6-402d-bce1-62b5631e8db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION OF TRACT UNDER-USAGE IN OTHER TRACT'S HOME DISTRICTS vs. being near a boundary\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractUse,nearEdge,marker='.' )\n",
    "ax.set(xlabel=\"tract use vs. expectation\", ylabel=\"number of unfilled wedges - max = \"+str(nWedges))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "6bb5c12a-a6a4-4cb1-9026-d227bc11c2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.15978194614429012 = WA std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAP4ElEQVR4nO3db4zlV13H8ffH3dbEgNawg9bdhalmC1bSYhkKaoRFg+x2H2xM0LQQCE3rpoYSfKDpxkR4wJMlRKOEls2mbhoSbaOhqSvd0kdoiXXJTklbum22GbeVDkvSacsfCyZ14euDe4vX6Z25v929M/fM3vcruen8zjlz55uTk/30/O5vzqSqkCSpNT816QIkSRrGgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1aaIBleRwkueSPN5x/B8meSLJiSR/v9b1SZImJ5P8Pagk7wJeAr5QVW8ZMXYH8A/A71TVd5K8vqqeW486JUnrb6I7qKp6EHhxsC3JryT5cpKHk3w1yZv7XX8E3FZV3+l/r+EkSRewFj+DOgR8rKreBvwpcHu//XLg8iT/luRYkl0Tq1CStOY2T7qAQUleA/wm8I9JXmn+6f5/NwM7gJ3ANuCrSd5SVd9d5zIlSeugqYCit6P7blW9dUjfInCsqv4HeDrJSXqBdXwd65MkrZOmbvFV1ffphc8fAKTnqn73vcB7+u1b6N3yOzWJOiVJa2/Sj5nfBfw78KYki0luBD4I3JjkUeAEsLc//AHghSRPAF8B/qyqXphE3ZKktTfRx8wlSVpJU7f4JEl6xcQektiyZUvNzs5O6sdLkhrx8MMPP19VM8vbJxZQs7OzzM/PT+rHS5IakeQ/h7V7i0+S1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1KTW/mChNBaz++9bse+ZA3vWsRJJ58odlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSSMDKsnhJM8leXyF/g8meaz/eijJVeMvU5I0bbrsoO4Edq3S/zTw7qq6EvgUcGgMdUmSptzIP/leVQ8mmV2l/6GBy2PAtjHUJUmacuP+DOpG4P6VOpPsSzKfZH5paWnMP1qSdCEZW0AleQ+9gLp1pTFVdaiq5qpqbmZmZlw/WpJ0ARp5i6+LJFcCdwC7q+qFcbynJGm6nfcOKskbgHuAD1XVU+dfkiRJHXZQSe4CdgJbkiwCnwQuAqiqg8AngNcBtycBOFNVc2tVsCRpOnR5iu/6Ef03ATeNrSJJkhjTZ1DSRjK7/74V+545sGcdK5G0Go86kiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNWlkQCU5nOS5JI+v0J8kn02ykOSxJFePv0xJ0rTpsoO6E9i1Sv9uYEf/tQ/4/PmXJUmadiMDqqoeBF5cZche4AvVcwy4JMml4ypQkjSdxvEZ1Fbg2YHrxX6bJEnnbBwBlSFtNXRgsi/JfJL5paWlMfxoSdKFahwBtQhsH7jeBpweNrCqDlXVXFXNzczMjOFHS5IuVOMIqCPAh/tP870T+F5VfXsM7ytJmmKbRw1IchewE9iSZBH4JHARQFUdBI4C1wILwA+BG9aqWGmtze6/b8W+Zw7sWcdKJI0MqKq6fkR/AR8dW0WSJOFJEpKkRhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJo086khq1Wrn5kna+NxBSZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmtQpoJLsSnIyyUKS/UP6fy7JPyd5NMmJJDeMv1RJ0jQZGVBJNgG3AbuBK4Drk1yxbNhHgSeq6ipgJ/CXSS4ec62SpCnSZQd1DbBQVaeq6mXgbmDvsjEFvDZJgNcALwJnxlqpJGmqdAmorcCzA9eL/bZBnwN+FTgNfAP4eFX9ePkbJdmXZD7J/NLS0jmWLEmaBl0CKkPaatn1+4BHgF8C3gp8LsnPvuqbqg5V1VxVzc3MzJxlqZKkadIloBaB7QPX2+jtlAbdANxTPQvA08Cbx1OiJGkadQmo48COJJf1H3y4DjiybMw3gd8FSPILwJuAU+MsVJI0XTaPGlBVZ5LcAjwAbAIOV9WJJDf3+w8CnwLuTPINercEb62q59ewbknSBW5kQAFU1VHg6LK2gwNfnwZ+b7ylSZKmmSdJSJKaZEBJkppkQEmSmmRASZKa1OkhCUkwu/++FfueObBnHSuRpoM7KElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTOgVUkl1JTiZZSLJ/hTE7kzyS5ESSfx1vmZKkabN51IAkm4DbgPcCi8DxJEeq6omBMZcAtwO7quqbSV6/RvVKkqZElx3UNcBCVZ2qqpeBu4G9y8Z8ALinqr4JUFXPjbdMSdK06RJQW4FnB64X+22DLgd+Psm/JHk4yYeHvVGSfUnmk8wvLS2dW8WSpKnQJaAypK2WXW8G3gbsAd4H/EWSy1/1TVWHqmququZmZmbOulhJ0vQY+RkUvR3T9oHrbcDpIWOer6ofAD9I8iBwFfDUWKqUJE2dLjuo48COJJcluRi4DjiybMw/Ab+dZHOSnwHeATw53lIlSdNk5A6qqs4kuQV4ANgEHK6qE0lu7vcfrKonk3wZeAz4MXBHVT2+loVLki5sXW7xUVVHgaPL2g4uu/4M8JnxlSZJmmaeJCFJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlq0uZJFyCtZnb/fZMuoZPV6nzmwJ51rES6cLiDkiQ1yYCSJDWpU0Al2ZXkZJKFJPtXGff2JD9K8v7xlShJmkYjAyrJJuA2YDdwBXB9kitWGPdp4IFxFylJmj5dHpK4BlioqlMASe4G9gJPLBv3MeCLwNvHWqG0wfkAhXRuutzi2wo8O3C92G/7iSRbgd8HDq72Rkn2JZlPMr+0tHS2tUqSpkiXgMqQtlp2/dfArVX1o9XeqKoOVdVcVc3NzMx0LFGSNI263OJbBLYPXG8DTi8bMwfcnQRgC3BtkjNVde84ipQkTZ8uAXUc2JHkMuBbwHXABwYHVNVlr3yd5E7gS4aTJOl8jAyoqjqT5BZ6T+dtAg5X1YkkN/f7V/3cSZKkc9HpqKOqOgocXdY2NJiq6iPnX5Ykadp5koQkqUkGlCSpSZ5mronbKCeWS1pf7qAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU3qFFBJdiU5mWQhyf4h/R9M8lj/9VCSq8ZfqiRpmowMqCSbgNuA3cAVwPVJrlg27Gng3VV1JfAp4NC4C5UkTZcuO6hrgIWqOlVVLwN3A3sHB1TVQ1X1nf7lMWDbeMuUJE2bLgG1FXh24Hqx37aSG4H7h3Uk2ZdkPsn80tJS9yolSVOnS0BlSFsNHZi8h15A3Tqsv6oOVdVcVc3NzMx0r1KSNHU2dxizCGwfuN4GnF4+KMmVwB3A7qp6YTzlSZKmVZeAOg7sSHIZ8C3gOuADgwOSvAG4B/hQVT019iqlKTS7/74V+545sGcdK5EmY2RAVdWZJLcADwCbgMNVdSLJzf3+g8AngNcBtycBOFNVc2tXtiTpQtdlB0VVHQWOLms7OPD1TcBN4y1NkjTNOgWUpLWx2m08adp51JEkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUn+HpS0AXkMkqaBOyhJUpMMKElSkwwoSVKTDChJUpN8SEK6wPgAhS4UBpQ0RQwvbSQGlCTA8FJ7DCitOf/mkaRz4UMSkqQmGVCSpCYZUJKkJhlQkqQm+ZCEpJF8wk+T4A5KktQkA0qS1CRv8UlqjrcUBQaUxsRfxtUwBo3OR6eASrIL+BtgE3BHVR1Y1p9+/7XAD4GPVNXXx1yrpAb5PydaKyMDKskm4DbgvcAicDzJkap6YmDYbmBH//UO4PP9/0rSUAabRumyg7oGWKiqUwBJ7gb2AoMBtRf4QlUVcCzJJUkurapvj71iTYz/oKgF53rbcNT69ZbjcJO8TdsloLYCzw5cL/Lq3dGwMVuB/xdQSfYB+/qXLyU5eVbVDrcFeH4M7zMNnKvunKvumpmrfHoy33sWmpmrcRjjnL1xWGOXgMqQtjqHMVTVIeBQh5/ZWZL5qpob53teqJyr7pyr7pyr7pyrs9Pl96AWge0D19uA0+cwRpKkzroE1HFgR5LLklwMXAccWTbmCPDh9LwT+J6fP0mSzsfIW3xVdSbJLcAD9B4zP1xVJ5Lc3O8/CByl94j5Ar3HzG9Yu5JfZay3DC9wzlV3zlV3zlV3ztVZSO/BO0mS2uJZfJKkJhlQkqQmbYiASrIryckkC0n2D+lPks/2+x9LcvUk6mxBh7nameR7SR7pvz4xiTpbkORwkueSPL5Cv+uqr8Ncua6AJNuTfCXJk0lOJPn4kDGuq66qqukXvQcz/gP4ZeBi4FHgimVjrgXup/f7WO8Evjbpuhueq53AlyZdawsv4F3A1cDjK/S7rrrPleuqNw+XAlf3v34t8JT/Xp37ayPsoH5y1FJVvQy8ctTSoJ8ctVRVx4BLkly63oU2oMtcqa+qHgReXGWI66qvw1wJqKpvV/+g7Kr6L+BJeqfqDHJddbQRAmqlY5TOdsw06DoPv5Hk0ST3J/m19SltQ3JdnR3X1YAks8CvA19b1uW66mgj/D2osR21NAW6zMPXgTdW1UtJrgXupXcKvV7NddWd62pAktcAXwT+pKq+v7x7yLe4robYCDsoj1rqbuQ8VNX3q+ql/tdHgYuSbFm/EjcU11VHrqv/k+QieuH0d1V1z5AhrquONkJAedRSdyPnKskv9v/AJEmuobcGXlj3SjcG11VHrque/hz8LfBkVf3VCsNcVx01f4uv2j9qqRkd5+r9wB8nOQP8N3Bd9R8tmjZJ7qL39NmWJIvAJ4GLwHW1XIe5cl31/BbwIeAbSR7pt/058AZwXZ0tjzqSJDVpI9zikyRNIQNKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUpP8FKYq0/VSg+WcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/statePop\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 *tractPop[t]/statePop\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "\n",
    "print(sdTractUse,\"=\",STATE,\"std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "121f9869-3d25-4594-93b3-64a57a95aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.16189300620952754 = std dev of PRECINCT usage.  Here is its weighted histogram\n",
      "this is a histogram of PRECINCT usage by precinct for WA\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  \n",
    "n_bins=50\n",
    "avgPrecinctUse = 0.\n",
    "stateVTDPop = np.sum(vtdPop)\n",
    "for p in range(nPrecincts):\n",
    "    avgPrecinctUse += precinctUse[p] * vtdPop[p]/stateVTDPop\n",
    "sumVarPrecinctUse = 0.\n",
    "for p in range(nPrecincts):\n",
    "    sumVarPrecinctUse += (precinctUse[p]-avgPrecinctUse)**2 *vtdPop[p]/stateVTDPop\n",
    "sdPrecinctUse = sumVarPrecinctUse ** 0.5\n",
    "\n",
    "print(sdPrecinctUse,\"= std dev of PRECINCT usage.  Here is its weighted histogram\")\n",
    "print(\"this is a histogram of PRECINCT usage by precinct for\",STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins, weights=vtdPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "f6f03dcb-c722-4789-b47f-fb7aa3be97bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops, avg =  9.3803\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgTLC = 0.\n",
    "usedTracts = 0.\n",
    "for t in range (nTracts):\n",
    "    if(tractLoopCounter[t] > 0):\n",
    "        avgTLC += tractLoopCounter[t]\n",
    "        usedTracts +=1.\n",
    "avgTLC = round(avgTLC / usedTracts, 4)\n",
    "print(\"here's a look at numerical stability - number of loops, avg = \",avgTLC)\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by 0.00863 0.40938 HD avg vs true 0.40074\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculated statewide Hispanic pct was 0.06302, should have been 0.06802\n",
      "this is a histogram of home-district VAP pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR home district pct Hispanic in a histogram\n",
    "print(\"calculated statewide Hispanic pct was {0}, should have been {1}\"\n",
    "      .format(round(stateHisp2,5), round(np.sum(tractHisp)/np.sum(tractVAP),5) ) )\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district VAP pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvHisp, bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],\n",
    "        weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1ab7704f-a226-45a9-98a6-d0684522c1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Hispanic for PA\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF HISPANIC VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT HISPANIC VOTE?\n",
    "n_bins = 20\n",
    "HispSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvHisp[t]*n_bins)\n",
    "    HispSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Hispanic for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,HispSeats,width=0.09 )\n",
    "#plt.scatter(binMid,HispSeats )\n",
    "ax.set(xlabel=\"pct Hispanic\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using a threshold of 0.35 the number of Hisp districts should be 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy home districts?\n",
    "minHisp1 = 0.35\n",
    "minHisp2 = 0.40\n",
    "sumHispDistricts = 0.\n",
    "for t in range(nTracts):\n",
    "    if (HDvHisp[t] > minHisp1 and tractPop[t] > minTractPop):\n",
    "        sumHispDistricts += tractPop[t]/avgDistrictPop\n",
    "        if (HDvHisp[t] < minHisp2):\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.text(tractCPx[t],tractCPy[t],'o',color='blue', fontsize= 12)\n",
    "print(\"using a threshold of\",minHisp1,\"the number of Hisp districts should be\",round(sumHispDistricts,3))\n",
    "x,y = origMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Black for GA\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF BLACK VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT BLACK VOTE?\n",
    "n_bins = 10\n",
    "BlackSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvBlack[t]*n_bins)\n",
    "    BlackSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Black for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,BlackSeats,width=0.09 )\n",
    "ax.set(xlabel=\"pct Black\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 4.701 GA seats out of 14\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #NEW FORMULATION based on ALABAMA\n",
    "isOppor = [0]*nTracts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nTracts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-HDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * HDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if tractPop[t] > minTractPop and HDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += HDweight[t]\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c5125128-e4bb-4337-bc5e-3f48d7cf404f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the GA map with Hisp+Black greater than  0.4 or even 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black+Hispanic heavy home districts?\n",
    "minMinor = 0.40 \n",
    "minMinor2 = 0.50\n",
    "print(\"Here's the\",STATE,\"map with Hisp+Black greater than \",minMinor,\"or even\",minMinor2)\n",
    "for t in range(nTracts):\n",
    "    if ((HDvBlack[t] + HDvHisp[t]) > minMinor and tractPop[t] > minTractPop):\n",
    "        if (HDvBlack[t] + HDvHisp[t]) > minMinor2 :\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "        else :            \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "58119ef8-13ef-4135-9843-6518025dda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  765136.2142857143\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT population in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "1f9101bd-108a-48cb-bee5-3f4e04c18132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of HD area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "cf95cc7c-e58a-4174-94f6-b6d4c7ccb0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "617384b8-1e0b-45a7-ae09-babe8dd9d429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to tract population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractPop,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"Census tract population\", ylabel=\"tractUse\")\n",
    "ax.set_xlim(left=0,right=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 3.363 3.142\n",
      "fractional expected GOP seats =  3.059  out of  10 seats for WA\n",
      "simpler expected GOP seats =  3.2104  out of  10 seats\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
